Question: Utilizing compressive sensing for real-time visual tracking tasks.

This article provides a comprehensive analysis, both theoretical and practical, illustrating that a greedy algorithm known as Orthogonal Matching Pursuit (OMP) is capable of accurately reconstructing a signal with m non-zero components in a d-dimensional space from merely O(m ln d) random linear measurements. This represents a significant enhancement over earlier findings, which necessitated O(m^2) measurements. The latest outcomes for OMP are on par with those achieved through another methodology, referred to as Basis Pursuit (BP). In certain contexts, OMP presents an advantage over BP by being more computationally efficient and simpler to execute, making it a compelling choice for tackling signal recovery issues, particularly in scenarios requiring real-time processing or resource-constrained environments.

**Question**: The effect of social networks on job search outcomes

**Documents**: Social Networks and the Dynamics of Labor Market Outcomes: Disentangling the Informativeness and Causal Effects of Social Proximity
Social networks are a key determinant of labor market outcomes. This paper develops a model to disentangle the informational and causal effects of social proximity. The model is based on a dynamic game of incomplete information where agents form social ties and use the information from their network to make labor market decisions. We estimate the model using a unique dataset that combines detailed information on individuals' social networks and labor market outcomes. Our results suggest that social networks have a significant impact on labor market outcomes, both through the information they provide and through direct causal effects. We find that the informational effect of social networks is stronger than the causal effect, but both are important in shaping labor market outcomes.

**Similar Documents**: This study delves into the multifaceted role of social networks in influencing labor market outcomes, specifically focusing on distinguishing between the informative and causal impacts of social proximity. A dynamic game-theoretic model with incomplete information is employed, in which individuals construct social connections and leverage network-derived information to guide their labor market choices. Utilizing a unique dataset that amalgamates granular data on individuals' social networks alongside their labor market results, we uncover that social networks wield a considerable force on labor market dynamics. Our findings reveal that social networks significantly affect labor market outcomes through both informational means and direct causality. Notably, the informational influence of social networks surpasses the causal effect, yet both mechanisms are crucial in molding labor market results, underscoring the importance of social ties in job search and career progression scenarios.

I ncreasingly powerful integrated circuits are making an entire range of new applications possible. Complementary metal-oxide semiconductor (CMOS) sensors, for example, have made the digital camera a commonplace consumer item. These light-sensitive chips, positioned where film would normally be, capture images as reusable digital files that users can upload to their computer, manipulate with software, and distribute electronically. Recent technological advances are enabling a new generation of smart cameras that represent a quantum leap in sophistication. While today's digital cameras capture images, smart cameras capture high-level descriptions of the scene and analyze what they see. These devices could support a wide variety of applications including human and animal detection, surveillance, motion analysis, and facial identification. Video processing has an insatiable demand for real-time performance. Fortunately, Moore's law provides an increasing pool of available computing power to apply to real-time analysis. Smart cameras leverage very large-scale integration (VLSI) to provide such analysis in a low-cost, low-power system with substantial memory. Moving well beyond pixel processing and compression, these systems run a wide range of algorithms to extract meaning from streaming video. Because they push the design space in so many dimensions, smart cameras are a leading-edge application for embedded system research. has developed a first-generation smart camera system that can detect people and analyze their movement in real time. Although there are many approaches to real-time video analysis, we chose to focus initially on human gesture recognition—identifying whether a subject is walking, standing, waving his arms, and so on. Because much work remains to be done on this problem, we sought to design an embedded system that can incorporate future algorithms as well as use those we created exclusively for this application. As Figure 1 shows, our algorithms use both low-level and high-level processing. The low-level component identifies different body parts and categorizes their movement in simple terms. The high-level component, which is application-dependent, uses this information to recognize each body part's action and the person's overall activity based on scenario parameters. The system captures images from the video input, which can be either uncompressed or compressed (MPEG and motion JPEG), and applies four different algorithms to detect and identify human body parts. Region extraction. The first algorithm transforms the pixels of an image, like that shown in Figure 2a, into an M × N bitmap and eliminates the background. It then detects the body part's skin area using a YUV …

A 352 288 pixel CMOS image sensor chip with perpixel single-slope ADC and dynamic memory in a standard digital 0.18m CMOS process is described. The chip performs “snapshot” image acquisition, parallel 8-bit A/D conversion, and digital readout at continuous rate of 10 000 frames/s or 1 Gpixels/s with power consumption of 50 mW. Each pixel consists of a photogate circuit, a three-stage comparator, and an 8-bit 3T dynamic memory comprising a total of 37 transistors in 9.4 9.4 m with a fill factor of 15%. The photogate quantum efficiency is 13.6%, and the sensor conversion gain is 13.1 V/e . At 1000 frames/s, measured integral nonlinearity is 0.22% over a 1-V range, rms temporal noise with digital CDS is 0.15%, and rms FPN with digital CDS is 0.027%. When operated at low frame rates, on-chip power management circuits permit complete powerdown between each frame conversion and readout. The digitized pixel data is read out over a 64-bit (8-pixel) wide bus operating at 167 MHz, i.e., over 1.33 GB/s. The chip is suitable for general high-speed imaging applications as well as for the implementation of several still and standard video rate applications that benefit from high-speed capture, such as dynamic range enhancement, motion estimation and compensation, and image stabilization.

The paper presents a system for automatic, geo-registered, real-time 3D reconstruction from video of urban scenes. The system collects video streams, as well as GPS and inertia measurements in order to place the reconstructed models in geo-registered coordinates. It is designed using current state of the art real-time modules for all processing steps. It employs commodity graphics hardware and standard CPU’s to achieve real-time performance. We present the main considerations in designing the system and the steps of the processing pipeline. Our system extends existing algorithms to meet the robustness and variability necessary to operate out of the lab. To account for the large dynamic range of outdoor videos the processing pipeline estimates global camera gain changes in the feature tracking stage and efficiently compensates for these in stereo estimation without impacting the real-time performance. The required accuracy for many applications is achieved with a two-step stereo reconstruction process exploiting the redundancy across frames. We show results on real video sequences comprising hundreds of thousands of frames.

The widespread use of vision-based video surveillance systems has inspired many research efforts on people localization. One of the current main trends in this field is based on probabilistic occupancy map (POM) obtained from multiple camera views. Although the POM-based approaches are robust against noisy foregrounds and can achieve great localization accuracy, they require high computation complexity. In this paper, two enhancement schemes are proposed to improve the efficiency of the POM-based people localization: (i) quick screening of potential people locations, and (ii) timely termination of iterations for occupancy probability estimation. Experimental results show that the proposed approach achieves up to 7.25 times speed-up compared to the standard POM-based approach, while delivering comparable people localization accuracy.

The concept of active tracking is presented to simulate the characteristics of human vision in intelligent visual surveillance. The Pan/Tilt/Zoom (PTZ) camera is generally used for active tracking. In this paper, we present a novel and effective approach for active object tracking with a PTZ camera, and construct a near real-time system for indoor and outdoor scenes. The tracking algorithm of our system is based on the feature matching, with the PID control to drive the camera. The feature extracted from moving people is described as a region covariance matrix which combines the spatial and statistical properties of the targets (e.g. coordinates, color, and gradient). Results from indoor and outdoor experiments demonstrate the effectiveness and accuracy of our approach.

In this paper, we describe how to establish an embedded framework for real-time top-view people counting. The development of our system consists of two parts, i.e. establishing an embedded signal processing platform and designing a people counting algorithm for the embedded system. For the hardware platform construction, we use Kinect as the camera and exploit NVIDIA Jetson TK1 board as the embedded processing platform. We describe how to build a channel to make Kinect for windows version 2.0 communicate with Jetson TK1. Based on the embedded system, we adapt a water filling based scheme for top-view people counting, which integrates head detection based on water drop, people tracking and counting. Gaussian Mixture Model is used to construct and update the background model. The moving people in each video frame are extracted using background subtraction method. Additionally, the water filling algorithm is used to segment head area as Region Of Interest(ROI). Tracking and counting people are performed by calculating the distance of ROI center point before and after the frame. The whole framework is flexible and practical for real-time application.

Simultaneous tracking of multiple persons in real-world environments is an active research field and several approaches have been proposed, based on a variety of features and algorithms. Recently, there has been a growing interest in organizing systematic evaluations to compare the various techniques. Unfortunately, the lack of common metrics for measuring the performance of multiple object trackers still makes it hard to compare their results. In this work, we introduce two intuitive and general metrics to allow for objective comparison of tracker characteristics, focusing on their precision in estimating object locations, their accuracy in recognizing object configurations and their ability to consistently label objects over time. These metrics have been extensively used in two large-scale international evaluations, the 2006 and 2007 CLEAR evaluations, to measure and compare the performance of multiple object trackers for a wide variety of tracking tasks. Selected performance results are presented and the advantages and drawbacks of the presented metrics are discussed based on the experience gained during the evaluations.

Visual saliency detection aims at identifying the most visually distinctive parts in an image, and serves as a pre-processing step for a variety of computer vision and image processing tasks. To this end, the saliency detection procedure must be as fast and compact as possible and optimally processes input images in a real time manner. It is an essential application requirement for the saliency detection task. However, contemporary detection methods often utilize some complicated procedures to pursue feeble improvements on the detection precession, which always take hundreds of milliseconds and make them not easy to be applied practically. In this paper, we tackle this problem by proposing a fast and compact saliency score regression network which employs fully convolutional network, a special deep convolutional neural network, to estimate the saliency of objects in images. It is an extremely simplified endto-end deep neural network without any pre-processings and post-processings. When given an image, the network can directly predict a dense full-resolution saliency map (image-to-image prediction). It works like a compact pipeline which effectively simplifies the detection procedure. Our method is evaluated on six public datasets, and experimental results show that it can achieve comparable or better precision performance than the state-of-the-art methods while get a significant improvement in detection speed (35 FPS, processing in real time).

We present a system for fast and highly accurate 3D localization of objects like cars in autonomous driving applications, using a single camera. Our localization framework jointly uses information from complementary modalities such as structure from motion (SFM) and object detection to achieve high localization accuracy in both near and far fields. This is in contrast to prior works that rely purely on detector outputs, or motion segmentation based on sparse feature tracks. Rather than completely commit to tracklets generated by a 2D tracker, we make novel use of raw detection scores to allow our 3D bounding boxes to adapt to better quality 3D cues. To extract SFM cues, we demonstrate the advantages of dense tracking over sparse mechanisms in autonomous driving scenarios. In contrast to complex scene understanding, our formulation for 3D localization is efficient and can be regarded as an extension of sparse bundle adjustment to incorporate object detection cues. Experiments on the KITTI dataset show the efficacy of our cues, as well as the accuracy and robustness of our 3D object localization relative to ground truth and prior works.

Robust visual tracking is a challenging computer vision problem, with many real-world applications. Most existing approaches employ hand-crafted appearance features, such as HOG or Color Names. Recently, deep RGB features extracted from convolutional neural networks have been successfully applied for tracking. Despite their success, these features only capture appearance information. On the other hand, motion cues provide discriminative and complementary information that can improve tracking performance. Contrary to visual tracking, deep motion features have been successfully applied for action recognition and video classification tasks. Typically, the motion features are learned by training a CNN on optical flow images extracted from large amounts of labeled videos. This paper presents an investigation of the impact of deep motion features in a tracking-by-detection framework. We further show that hand-crafted, deep RGB, and deep motion features contain complementary information. To the best of our knowledge, we are the first to propose fusing appearance information with deep motion features for visual tracking. Comprehensive experiments clearly suggest that our fusion approach with deep motion features outperforms standard methods relying on appearance information alone.


Question: Utilizing compressive sensing for real-time visual tracking tasks.

This document introduces the Orthogonal Matching Pursuit (OMP) algorithm, an enhanced version of the Matching Pursuit (MP) technique developed by Mallat and Zhang (1992), designed for the computation of function representations using nonorthogonal and potentially overcomplete dictionaries. The OMP algorithm is particularly focused on maintaining full backward orthogonality of the residual during the approximation process, leading to improved accuracy and efficiency. The algorithm's steps are as follows: beginning with initial values of Ro f = f, fo = 0, and k = 1, the algorithm proceeds to compute the inner products of the current residual (Rkf) with the dictionary elements (Zn). The next step involves identifying the element nktl that maximizes the inner product (Rkf, 'nk+I) l 2. This maximization process ensures that the algorithm selects the dictionary element that most significantly reduces the residual error at each iteration. The recursive nature of the algorithm allows for all additional computations required for the OMP algorithm to be performed efficiently. The OMP algorithm, with its emphasis on backward orthogonality and recursive computation, offers a more precise and computationally effective approach to function approximation compared to its predecessor, particularly when dealing with nonorthogonal and overcomplete dictionaries.

The document's relationship to the question about real-time visual tracking using compressive sensing is that both involve advanced signal processing techniques. While the provided document focuses on function approximation using OMP, real-time visual tracking using compressive sensing also relies on efficient signal representation and processing. Compressive sensing, a method for acquiring and reconstructing sparse signals, can be applied in visual tracking to reduce the amount of data that needs to be processed in real-time, making it more computationally feasible. The OMP algorithm, by providing a method for efficient signal representation, can potentially be adapted or integrated into compressive sensing frameworks to enhance the performance of real-time visual tracking systems.

This paper presents a method of estimating camera pose in an unknown scene. While this has previously been attempted by adapting SLAM algorithms developed for robotic exploration, we propose a system specifically designed to track a hand-held camera in a small AR workspace. We propose to split tracking and mapping into two separate tasks, processed in parallel threads on a dual-core computer: one thread deals with the task of robustly tracking erratic hand-held motion, while the other produces a 3D map of point features from previously observed video frames. This allows the use of computationally expensive batch optimisation techniques not usually associated with real-time operation: The result is a system that produces detailed maps with thousands of landmarks which can be tracked at frame-rate, with an accuracy and robustness rivalling that of state-of-the-art model-based systems.

When applying sparse representation techniques to images, the standard approach is to independently compute the representations for a set of overlapping image patches. This method performs very well in a variety of applications, but results in a representation that is multi-valued and not optimized with respect to the entire image. An alternative representation structure is provided by a convolutional sparse representation, in which a sparse representation of an entire image is computed by replacing the linear combination of a set of dictionary vectors by the sum of a set of convolutions with dictionary filters. The resulting representation is both single-valued and jointly optimized over the entire image. While this form of a sparse representation has been applied to a variety of problems in signal and image processing and computer vision, the computational expense of the corresponding optimization problems has restricted application to relatively small signals and images. This paper presents new, efficient algorithms that substantially improve on the performance of other recent methods, contributing to the development of this type of representation as a practical tool for a wider range of problems.

Object detection is one of the most important topics in computer vision task and has obtained impressive performance thanks to the use of deep convolutional neural network. For object detection, especially in still image, it has achieved excellent performance during past two years, such as the series of R-CNN which plays a vital role in improving performance. However, with the number of surveillance videos increasing, the current methods may not meet the growing demand. In this paper, we propose a new framework named moving-object proposals generation and prediction framework (MPGP) to reduce the searching space and generate some accurate proposals which can reduce computational cost. In addition, we explore the relation of moving regions in feature map of different layers and predict candidates according to the results of previous frames. Last but not least, we utilize spatial-temporal information to strengthen the detection score and further adjust the location of the bounding boxes. Our MPGP framework can be applied to different region-based networks. Experiments on CUHK data set, XJTU data set, and AVSS data set, show that our approach outperforms the state-of-the-art approaches.

We present a system for fast and highly accurate 3D localization of objects like cars in autonomous driving applications, using a single camera. Our localization framework jointly uses information from complementary modalities such as structure from motion (SFM) and object detection to achieve high localization accuracy in both near and far fields. This is in contrast to prior works that rely purely on detector outputs, or motion segmentation based on sparse feature tracks. Rather than completely commit to tracklets generated by a 2D tracker, we make novel use of raw detection scores to allow our 3D bounding boxes to adapt to better quality 3D cues. To extract SFM cues, we demonstrate the advantages of dense tracking over sparse mechanisms in autonomous driving scenarios. In contrast to complex scene understanding, our formulation for 3D localization is efficient and can be regarded as an extension of sparse bundle adjustment to incorporate object detection cues. Experiments on the KITTI dataset show the efficacy of our cues, as well as the accuracy and robustness of our 3D object localization relative to ground truth and prior works.

RECENTLY , several clustering algorithms have been used to solve variety of problems from different discipline. This dissertation aims to address different challenging tasks in computer vision and pattern recognition by casting the problems as a clustering problem. We proposed novel approaches to solve multi-target tracking, visual geo-localization and outlier detection problems using a unified underlining clustering framework, i.e., dominant set clustering and its extensions, and presented a superior result over several state-of-the-art approaches. Firstly, this dissertation will present a new framework for multi-target tracking in a single camera. We proposed a novel data association technique using Dominant set clustering (DCS) framework. We formulate the tracking task as finding dominant sets on the constructed undirected edge weighted graph. Unlike most techniques which are limited in temporal locality (i.e. few frames are considered), we utilized a pairwise relationships (in appearance and position) between different detections across the whole temporal span of the video for data association in a global manner. Meanwhile, temporal sliding window technique is utilized to find tracklets and perform further merging on them. Our robust tracklet merging step renders our tracker to long term occlusions with more robustness. DSC leads us to a more accurate approach to multi-object tracking by considering all the pairwise relationships in a batch of frames; however, it has some limitations. Firstly, it finds target trajectories(clusters) one-by-one (peel off strategy), causing change in the scale of the problem. Secondly, the algorithm used to enumerate dominant sets, i.e., replicator dynamics, have quadratic computational complexity, which makes it impractical on larger graphs. To address these problems and extend tracking problem to multiple non-overlapping cameras, we proposed a novel and unified three-layer hierarchical approach. Given a video and a set of detections (obtained by any person detector), we first solve withincamera tracking employing the first two layers of our framework and, then, in the third layer, we solve across-camera tracking by merging tracks of the same person in all cameras in a simultaneous fashion. To best serve our purpose, a constrained dominant set clustering (CDSC) technique, a parametrized version of standard quadratic optimization, is employed to solve both tracking tasks. The tracking problem is caste as

A revolutionary type of imaging device, known as a silicon retina or event-based sensor, has recently been developed and is gaining in popularity in the field of artificial vision systems. These devices are inspired by a biological retina and operate in a significantly different way to traditional CCD-based imaging sensors. While a CCD produces frames of pixel intensities, an event-based sensor produces a continuous stream of events, each of which is generated when a pixel detects a change in log light intensity. These pixels operate asynchronously and independently, producing an event-based output with high temporal resolution. There are also no fixed exposure times, allowing these devices to offer a very high dynamic range independently for each pixel. Additionally, these devices offer high-speed, low power operation and a sparse spatiotemporal output. As a consequence, the data from these sensors must be interpreted in a significantly different way to traditional imaging sensors and this paper explores the advantages this technology provides for space imaging. The applicability and capabilities of event-based sensors for SSA applications are demonstrated through telescope field trials. Trial results have confirmed that the devices are capable of observing resident space objects from LEO through to GEO orbital regimes. Significantly, observations of RSOs were made during both day-time and nighttime (terminator) conditions without modification to the camera or optics. The event based sensor’s ability to image stars and satellites during day-time hours offers a dramatic capability increase for terrestrial optical sensors. This paper shows the field testing and validation of two different architectures of event-based imaging sensors. An eventbased sensor’s asynchronous output has an intrinsically low data-rate. In addition to low-bandwidth communications requirements, the low weight, low-power and high-speed make them ideally suitable to meeting the demanding challenges required by space-based SSA systems. Results from these experiments and the systems developed highlight the applicability of event-based sensors to ground and space-based SSA tasks.

A lane detection system is an important component of many intelligent transportation systems. We present a robust realtime lane tracking algorithm for a curved local road. First, we present a comparative study to find a good realtime lane marking classifier. Once lane markings are detected, they are grouped into many lane boundary hypotheses represented by constrained cubic spline curves. We present a robust hypothesis generation algorithm using a particle filtering technique and a RANSAC (random sample concensus) algorithm. We introduce a probabilistic approach to group lane boundary hypotheses into left and right lane boundaries. The proposed grouping approach can be applied to general part-based object tracking problems. It incorporates a likelihood-based object recognition technique into a Markov-style process. An experimental result on local streets shows that the suggested algorithm is very reliable

We propose a method which can perform real-time 3D reconstruction from a single hand-held event camera with no additional sensing, and works in unstructured scenes of which it has no prior knowledge. It is based on three decoupled probabilistic filters, each estimating 6-DoF camera motion, scene logarithmic (log) intensity gradient and scene inverse depth relative to a keyframe, and we build a real-time graph of these to track and model over an extended local workspace. We also upgrade the gradient estimate for each keyframe into an intensity image, allowing us to recover a real-time video-like intensity sequence with spatial and temporal super-resolution from the low bit-rate input event stream. To the best of our knowledge, this is the first algorithm provably able to track a general 6D motion along with reconstruction of arbitrary structure including its intensity and the reconstruction of grayscale video that exclusively relies on event camera data.

We propose a linear programming relaxation scheme for the class of multiple object tracking problems where the inter-object interaction metric is convex and the intra-object term quantifying object state continuity may use any metric. The proposed scheme models object tracking as a multi-path searching problem. It explicitly models track interaction, such as object spatial layout consistency or mutual occlusion, and optimizes multiple object tracks simultaneously. The proposed scheme does not rely on track initialization and complex heuristics. It has much less average complexity than previous efficient exhaustive search methods such as extended dynamic programming and is found to be able to find the global optimum with high probability. We have successfully applied the proposed method to multiple object tracking in video streams.

This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called the “Integral Image” which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features from a larger set and yields extremely efficient classifiers[6]. The third contribution is a method for combining increasingly more complex classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. The cascade can be viewed as an object specific focus-of-attention mechanism which unlike previous approaches provides statistical guarantees that discarded regions are unlikely to contain the object of interest. In the domain of face detection the system yields detection rates comparable to the best previous systems. Used in real-time applications, the detector runs at 15 frames per second without resorting to image differencing or skin color detection.


Question: Utilizing compressive sensing for real-time visual tracking tasks.

This paper offers a comprehensive review of sequential simulation techniques for drawing samples from posterior distributions, a critical aspect of Bayesian filtering in non-linear, non-Gaussian discrete time dynamic models. A broad framework for importance sampling is outlined, integrating and expanding upon various methodologies developed across diverse scientific domains over recent decades. Among the innovations presented, the paper highlights the integration of local linearization techniques, akin to those previously utilized in deterministic filtering literature, which significantly enhances the efficacy of importance sampling distributions. Additionally, a strategy incorporating Rao-Blackwellization is described, designed to exploit the analytical structure inherent in certain state-space models. The paper concludes with the development of algorithms for prediction, smoothing, and likelihood evaluation in dynamic models, showcasing the versatility and power of these sequential Monte Carlo sampling methods.

**Question**: A Hybrid Genetic Algorithm-Particle Swarm Optimization Approach for the Design of Recurrent Neural/Fuzzy Networks

**Documents**: A Novel Approach for Real-time Visual Tracking using Compressive Sensing
In this paper, we propose a novel algorithm for real-time visual tracking using compressive sensing. Compressive sensing is a signal processing technique that allows the reconstruction of sparse or compressible signals from a small number of measurements. The proposed algorithm combines compressive sensing with a particle filter to track objects in real-time. The particle filter is used to estimate the state of the object being tracked, while compressive sensing is used to compress the image data and reduce the computational load. The proposed algorithm is evaluated on a dataset of real-world video sequences and compared to existing state-of-the-art tracking algorithms. The results show that the proposed algorithm outperforms the existing algorithms in terms of accuracy and computational efficiency.

**Similar Documents**: This paper introduces an innovative real-time visual tracking algorithm that leverages compressive sensing for enhanced performance and efficiency. Compressive sensing, a powerful signal processing method, enables the reconstruction of sparse or compressible signals from limited measurements. By integrating this technique with a particle filter, the proposed algorithm achieves real-time object tracking. The particle filter is employed to estimate the object's state, while compressive sensing is applied to compress image data, significantly reducing computational demands. The algorithm's efficacy is demonstrated through evaluation on a comprehensive set of real-world video sequences, where it surpasses current state-of-the-art tracking algorithms in both accuracy and computational efficiency.

We present a mobile vision system for multi-person tracking in busy environments. Specifically, the system integrates continuous visual odometry computation with tracking-by-detection in order to track pedestrians in spite of frequent occlusions and egomotion of the camera rig. To achieve reliable performance under real-world conditions, it has long been advocated to extract and combine as much visual information as possible. We propose a way to closely integrate the vision modules for visual odometry, pedestrian detection, depth estimation, and tracking. The integration naturally leads to several cognitive feedback loops between the modules. Among others, we propose a novel feedback connection from the object detector to visual odometry which utilizes the semantic knowledge of detection to stabilize localization. Feedback loops always carry the danger that erroneous feedback from one module is amplified and causes the entire system to become instable. We therefore incorporate automatic failure detection and recovery, allowing the system to continue when a module becomes unreliable. The approach is experimentally evaluated on several long and difficult video sequences from busy inner-city locations. Our results show that the proposed integration makes it possible to deliver stable tracking performance in scenes of previously infeasible complexity.

We consider the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise. We cast the recognition problem as one of classifying among multiple linear regression models and argue that new theory from sparse signal representation offers the key to addressing this problem. Based on a sparse representation computed by C1-minimization, we propose a general classification algorithm for (image-based) object recognition. This new framework provides new insights into two crucial issues in face recognition: feature extraction and robustness to occlusion. For feature extraction, we show that if sparsity in the recognition problem is properly harnessed, the choice of features is no longer critical. What is critical, however, is whether the number of features is sufficiently large and whether the sparse representation is correctly computed. Unconventional features such as downsampled images and random projections perform just as well as conventional features such as eigenfaces and Laplacianfaces, as long as the dimension of the feature space surpasses certain threshold, predicted by the theory of sparse representation. This framework can handle errors due to occlusion and corruption uniformly by exploiting the fact that these errors are often sparse with respect to the standard (pixel) basis. The theory of sparse representation helps predict how much occlusion the recognition algorithm can handle and how to choose the training images to maximize robustness to occlusion. We conduct extensive experiments on publicly available databases to verify the efficacy of the proposed algorithm and corroborate the above claims.

Model-based recognition and motion tracking depends upon the ability to solve for projection and model parameters that will best fit a 3-D model to matching 2-D image features. This paper extends current methods of parameter solving to handle objects with arbitrary curved surfaces and with any number of internal parameters representing articulations, variable dimensions, or surface deformations. Numerical stabilization methods are developed that take account of inherent inaccuracies in the image measurements and allow useful solutions to be determined even when there are fewer matches than unknown parameters. The LevenbergMarquardt method is used to always ensure convergence of the solution. These techniques allow model-based vision to be used for a much wider class of problems than was possible with previous methods. Their application is demonstrated for tracking the motion of curved, parameterized objects. This paper has been published in IEEE Transactions on Pattern Analysis and Machine Intelligence, 13, 5 (May 1991), pp. 441–450.

We present a new motion tracking technique to robustly reconstruct non-rigid geometries and motions from a single view depth input recorded by a consumer depth sensor. The idea is based on the observation that most non-rigid motions (especially human-related motions) are intrinsically involved in articulate motion subspace. To take this advantage, we propose a novel <inline-formula><tex-math notation="LaTeX">$L_0$</tex-math><alternatives> <inline-graphic xlink:href="xu-ieq2-2688331.gif"/></alternatives></inline-formula> based motion regularizer with an iterative solver that implicitly constrains local deformations with articulate structures, leading to reduced solution space and physical plausible deformations. The <inline-formula><tex-math notation="LaTeX">$L_0$</tex-math> <alternatives><inline-graphic xlink:href="xu-ieq3-2688331.gif"/></alternatives></inline-formula> strategy is integrated into the available non-rigid motion tracking pipeline, and gradually extracts articulate joints information online with the tracking, which corrects the tracking errors in the results. The information of the articulate joints is used in the following tracking procedure to further improve the tracking accuracy and prevent tracking failures. Extensive experiments over complex human body motions with occlusions, facial and hand motions demonstrate that our approach substantially improves the robustness and accuracy in motion tracking.

In image sequence processing kalman filtering is used for an adaptive background estimation, in order to separate the foreground from the background. The presented work is an approach which takes into account that changing illumination should be considered in the background estimation, and should not be detected as foreground. The new approach assumes a stationary CCD cameras with fixed focal length and considers non-rigid objects moving non-continuously like human bodies. Furthermore, statistic based methods are used to overcome the problems caused by shadow borders and the adaptation, when the background is covered by the foreground.

In this paper, we present a moving object detection system named Flux Tensor with Split Gaussian models (FTSG) that exploits the benefits of fusing a motion computation method based on spatio-temporal tensor formulation, a novel foreground and background modeling scheme, and a multi-cue appearance comparison. This hybrid system can handle challenges such as shadows, illumination changes, dynamic background, stopped and removed objects. Extensive testing performed on the CVPR 2014 Change Detection benchmark dataset shows that FTSG outperforms state-of-the-art methods.

The goal of our work is to provide a fast and accurate method to estimate the camera motion from RGB-D images. Our approach registers two consecutive RGB-D frames directly upon each other by minimizing the photometric error. We estimate the camera motion using non-linear minimization in combination with a coarse-to-fine scheme. To allow for noise and outliers in the image data, we propose to use a robust error function that reduces the influence of large residuals. Furthermore, our formulation allows for the inclusion of a motion model which can be based on prior knowledge, temporal filtering, or additional sensors like an IMU. Our method is attractive for robots with limited computational resources as it runs in real-time on a single CPU core and has a small, constant memory footprint. In an extensive set of experiments carried out both on a benchmark dataset and synthetic data, we demonstrate that our approach is more accurate and robust than previous methods. We provide our software under an open source license.

Object detection is one of the most important topics in computer vision task and has obtained impressive performance thanks to the use of deep convolutional neural network. For object detection, especially in still image, it has achieved excellent performance during past two years, such as the series of R-CNN which plays a vital role in improving performance. However, with the number of surveillance videos increasing, the current methods may not meet the growing demand. In this paper, we propose a new framework named moving-object proposals generation and prediction framework (MPGP) to reduce the searching space and generate some accurate proposals which can reduce computational cost. In addition, we explore the relation of moving regions in feature map of different layers and predict candidates according to the results of previous frames. Last but not least, we utilize spatial-temporal information to strengthen the detection score and further adjust the location of the bounding boxes. Our MPGP framework can be applied to different region-based networks. Experiments on CUHK data set, XJTU data set, and AVSS data set, show that our approach outperforms the state-of-the-art approaches.

In general, the problem of change detection is studied in color space. Most proposed methods aim at dynamically finding the best color thresholds to detect moving objects against a background model. Background models are often complex to handle noise affecting pixels. Because the pixels are considered individually, some changes cannot be detected because it involves groups of pixels and some individual pixels may have the same appearance as the background. To solve this problem, we propose to formulate the problem of background subtraction in feature space. Instead of comparing the color of pixels in the current image with colors in a background model, features in the current image are compared with features in the background model. The use of a feature at each pixel position allows accounting for change affecting groups of pixels, and at the same time adds robustness to local perturbations. With the advent of binary feature descriptors such as BRISK or FREAK, it is now possible to use features in various applications at low computational cost. We thus propose to perform background subtraction with a small binary descriptor that we named Local Binary Similarity Patterns (LBSP). We show that this descriptor outperforms color, and that a simple background subtractor using LBSP outperforms many sophisticated state of the art methods in baseline scenarios.

An event camera is a silicon retina which outputs not a sequence of video frames like a standard camera, but a stream of asynchronous spikes, each with pixel location, sign and precise timing, indicating when individual pixels record a threshold log intensity change. By encoding only image change, it offers the potential to transmit the information in a standard video but at vastly reduced bitrate, and with huge added advantages of very high dynamic range and temporal resolution. However, event data calls for new algorithms, and in particular we believe that algorithms which incrementally estimate global scene models are best placed to take full advantages of its properties. Here, we show for the first time that an event stream, with no additional sensing, can be used to track accurate camera rotation while building a persistent and high quality mosaic of a scene which is super-resolution accurate and has high dynamic range. Our method involves parallel camera rotation tracking and template reconstruction from estimated gradients, both operating on an event-by-event basis and based on probabilistic filtering.


Question: Utilizing compressive sensing for real-time visual tracking applications.

This paper explores the concept of Compressed Sensing (CS), as initially outlined in [14] and further developed in [20, 3, 4]. CS proposes that signals or images, although unknown, can be compressed using a known transformation (such as wavelet or Fourier) and can be accurately reconstructed from fewer measurements than the total number of pixels. The measurements taken are non-adaptive, consisting of 'random' linear combinations of the transform coefficients. Reconstruction is achieved by solving for the transform coefficients that are consistent with the measured data and have the smallest possible ` norm. Our series of numerical experiments generally confirm the foundational concept of CS, particularly in cases where the transform coefficients are sparse, meaning the majority are zero. We then explore scenarios where the object has all coefficients non-zero but follows an ` bound for some p ∈ (0, 1]. Our experiments indicate that the underlying inequalities of the CS method involve reasonable constants. We proceed to examine synthetic examples representing spectroscopy and image processing challenges, observing that reconstructions from CS can often appear visually "noisy". Post-processing using translation-invariant de-noising significantly improves the visual appearance. We also consider a multiscale application of compressed sensing, where different scales are processed separately, leading to significantly higher quality reconstructions than a direct application of the CS method. Additionally, we find that various families of 'random' linear combinations, including random spherical, random signs, partial Fourier, and partial Hadamard, all perform equally well. These findings underscore that, when applied in the right context, the CS framework can substantially reduce the need for traditional sampling, with many practical extensions of the core concept.

We present the first dense SLAM system capable of reconstructing non-rigidly deforming scenes in real-time, by fusing together RGBD scans captured from commodity sensors. Our DynamicFusion approach reconstructs scene geometry whilst simultaneously estimating a dense volumetric 6D motion field that warps the estimated geometry into a live frame. Like KinectFusion, our system produces increasingly denoised, detailed, and complete reconstructions as more measurements are fused, and displays the updated model in real time. Because we do not require a template or other prior scene model, the approach is applicable to a wide range of moving objects and scenes.

Online 3D reconstruction of real-world scenes has been attracting increasing interests from both the academia and industry, especially with the consumer-level depth cameras becoming widely available. Recent most online reconstruction systems take live depth data from a moving Kinect camera and incrementally fuse them to a single high-quality 3D model in real time. Although most real-world scenes have static environment, the daily objects in a scene often move dynamically, which are non-trivial to reconstruct especially when the camera is also not still. To solve this problem, we propose a single depth camera-based real-time approach for simultaneous reconstruction of dynamic object and static environment, and provide solutions for its key issues. In particular, we first introduce a robust optimization scheme which takes advantage of raycasted maps to segment moving object and background from the live depth map. The corresponding depth data are then fused to the volumes, respectively. These volumes are raycasted to extract views of the implicit surface which can be used as a consistent reference frame for the next iteration of segmentation and tracking. Particularly, in order to handle fast motion of dynamic object and handheld camera in the fusion stage, we propose a sequential 6D pose prediction method which largely increases the registration robustness and avoids registration failures occurred in conventional methods. Experimental results show that our approach can reconstruct moving object as well as static environment with rich details, and outperform conventional methods in multiple aspects.

■ Compact, efficient and easy to program digital signal processors execute algorithms once too computationally expensive for real-time applications. ■ Modeling and simulation programs more accurately predict the effects that physical ocean parameters have on the performance of imaging systems under different geometric configurations. ■ Image processing algorithms that handle data from multiple synchronous sources and that can extract and match feature points from each such source derive accurate 3-D scene information. ■ Digital compression schemes provide high-quality standardizations for increased data transfer rates (i.e. streaming video) and reduced storage requirements. This paper reports developments over the past three years in the following topics: ■ Image formation and image processing methods; ■ Extended range imaging techniques;

Designing effective feature and handling the model drift problem are two important aspects for online visual tracking. For feature representation, gradient and color features are most widely used, but how to effectively combine them for visual tracking is still an open problem. In this paper, we propose a rich feature descriptor, MC-HOG, by leveraging rich gradient information across multiple color channels or spaces. Then MC-HOG features are embedded into the correlation tracking framework to estimate the state of the target. For handling the model drift problem caused by occlusion or distracter, we propose saliency proposals as prior information to provide candidates and reduce background interference. In addition to saliency proposals, a ranking strategy is proposed to determine the importance of these proposals by exploiting the learnt appearance filter, historical preserved object samples and the distracting proposals. In this way, the proposed approach could effectively explore the color-gradient characteristics and alleviate the model drift problem. Extensive evaluations performed on the benchmark dataset show the superiority of the proposed method.

Object occlusion is widespread in video surveillance due to the influence of angle and environment, which brings a great impact on the target tracking and makes the development of video object tracking encountered meeting many constraints. The challenge in video object tracking is how to track accurately when the target is obscured. We use the frame difference method to detect the target and smooth the target trajectory by using Kalman filter. When the target is occluded, we can select the appropriate fitting section after smoothing trajectories and use the least square method to fit the target motion path adaptively, so we can predict the object location. By comparing the experimental results with the actual motion of the target, it shows that the algorithm can be used to track most of the occlusion targets precisely.

We describe an online approach to learn non-linear motion patterns and robust appearance models for multi-target tracking in a tracklet association framework. Unlike most previous approaches that use linear motion methods only, we online build a non-linear motion map to better explain direction changes and produce more robust motion affinities between tracklets. Moreover, based on the incremental learned entry/exit map, a multiple instance learning method is devised to produce strong appearance models for tracking; positive sample pairs are collected from different track-lets so that training samples have high diversity. Finally, using online learned moving groups, a tracklet completion process is introduced to deal with tracklets not reaching entry/exit points. We evaluate our approach on three public data sets, and show significant improvements compared with state-of-art methods.

In this paper we describe a method that estimates the motion of a calibrated camera (settled on an experimental vehicle) and the tridimensional geometry of the environment. The only data used is a video input. In fact, interest points are tracked and matched between frames at video rate. Robust estimates of the camera motion are computed in real-time, key-frames are selected and permit the features 3D reconstruction. The algorithm is particularly appropriate to the reconstruction of long images sequences thanks to the introduction of a fast and local bundle adjustment method that ensures both good accuracy and consistency of the estimated camera poses along the sequence. It also largely reduces computational complexity compared to a global bundle adjustment. Experiments on real data were carried out to evaluate speed and robustness of the method for a sequence of about one kilometer long. Results are also compared to the ground truth measured with a differential GPS.

Tracking-by-learning strategies have been effective in solving many challenging problems in visual tracking, in which the learning sample generation and labeling play important roles for final performance. Since the concern of deep learning based approaches has shown an impressive performance in different vision tasks, how to properly apply the learning model, such as CNN, to an online tracking framework is still challenging. In this paper, to overcome the overfitting problem caused by straight-forward incorporation, we propose an online tracking framework by constructing a CNN based adaptive appearance model to generate more reliable training data over time. With a reformative Metropolis-Hastings re-sampling scheme to reshape particles for a better state posterior representation during online learning, the proposed tracking outperforms most of the state-of-art trackers on challenging benchmark video sequences.

With the availability of low-cost micro aerial vehicles (MAVs), unmanned aerial vehicles (UAVs) quickly gain popularity and application potential. This requires techniques that can be understood by non-experts and flexibly applied for rapid prototyping. Visual tracking is an essential task with many applications, such as autonomous navigation and scene acquisition. While marker-less methods emerge, marker-based methods still have major advantages including simplicity, robustness, accuracy and performance. In practice, however, multi-marker setups introduce complexity and calibration efforts that can void the advantages. This work proposes a solution for practical, robust and easy-to-use marker-based tracking with independent compound targets. We introduce two novel target designs and describe pose estimation, noise removal and geometric transformations. The concepts are implemented in a tracking library for the Parrot AR. Drone 2.0. We explain its video access and camera calibration, and provide a first set of intrinsic parameters, jointly estimated from 14 units with high accuracy and low variance. The library is applied in a one-day contest on automatic visual navigation of UAVs, where students without technical background and programming skills achieved learning by experience and rapid development. This shows the effectiveness of combining capability with simplicity, and provides a case study on robotics in interdisciplinary education.

MOVING OBJECT DETECTION, TRACKING AND CLASSIFICATION FOR SMART VIDEO SURVEILLANCE Yiğithan Dedeoğlu M.S. in Computer Engineering Supervisor: Assist. Prof. Dr. Uğur Güdükbay August, 2004 Video surveillance has long been in use to monitor security sensitive areas such as banks, department stores, highways, crowded public places and borders. The advance in computing power, availability of large-capacity storage devices and high speed network infrastructure paved the way for cheaper, multi sensor video surveillance systems. Traditionally, the video outputs are processed online by human operators and are usually saved to tapes for later use only after a forensic event. The increase in the number of cameras in ordinary surveillance systems overloaded both the human operators and the storage devices with high volumes of data and made it infeasible to ensure proper monitoring of sensitive areas for long times. In order to filter out redundant information generated by an array of cameras, and increase the response time to forensic events, assisting the human operators with identification of important events in video by the use of “smart” video surveillance systems has become a critical requirement. The making of video surveillance systems “smart” requires fast, reliable and robust algorithms for moving object detection, classification, tracking and activity analysis. In this thesis, a smart visual surveillance system with real-time moving object detection, classification and tracking capabilities is presented. The system operates on both color and gray scale video imagery from a stationary camera. It can handle object detection in indoor and outdoor environments and under changing illumination conditions. The classification algorithm makes use of the shape of the detected objects and temporal tracking results to successfully categorize objects into pre-defined classes like human, human group and vehicle. The system is also able to detect the natural phenomenon fire in various scenes reliably. The proposed tracking algorithm successfully tracks video objects even in full occlusion cases. In addition to these, some important needs of a robust iii


Question: Utilizing compressive sensing for real-time visual tracking applications.

This paper delves into the critical question of reconstructing an object from a limited set of frequency samples, a problem that is foundational in signal processing. The central inquiry is whether it's feasible to accurately reconstruct a discrete-time signal, represented as f ∈ C^N, given only a randomly selected subset of its Fourier coefficients, denoted by Ω. A key finding presented here revolves around signals that are sparse in the sense of being superpositions of spikes, with f(t) = ∑_{τ ∈ T} f(τ)δ(t − τ), where T represents the set of spike locations. If the number of spikes, |T|, is bounded by a constant multiple of the logarithm of N times the size of the frequency set, |Ω|, it is shown that the signal f can indeed be reconstructed with high probability through an optimization problem. Specifically, by solving a convex optimization problem, the signal can be exactly recovered. This approach is effective for almost all sets of frequencies, provided they are of size O(|T| log N). Moreover, this is shown to be nearly optimal, as any other method with a similar success probability would require a frequency sample size proportional to |T| log N. This work not only establishes a new type of nonlinear sampling theorem but also extends to various other scenarios and higher dimensions. For instance, the reconstruction of piecewise constant objects in one or two dimensions from incomplete frequency samples is demonstrated, given that the number of discontinuities adheres to the aforementioned condition, by minimizing convex functionals such as the total variation of f.

The methodology presented herein offers a robust framework for reconstructing signals from incomplete frequency information, leveraging convex optimization to achieve exact recovery under certain sparsity conditions. This has implications for a range of applications, including real-time visual tracking using compressive sensing, where efficient and accurate signal reconstruction from limited data is crucial.

When applying sparse representation techniques to images, the standard approach is to independently compute the representations for a set of overlapping image patches. This method performs very well in a variety of applications, but results in a representation that is multi-valued and not optimized with respect to the entire image. An alternative representation structure is provided by a convolutional sparse representation, in which a sparse representation of an entire image is computed by replacing the linear combination of a set of dictionary vectors by the sum of a set of convolutions with dictionary filters. The resulting representation is both single-valued and jointly optimized over the entire image. While this form of a sparse representation has been applied to a variety of problems in signal and image processing and computer vision, the computational expense of the corresponding optimization problems has restricted application to relatively small signals and images. This paper presents new, efficient algorithms that substantially improve on the performance of other recent methods, contributing to the development of this type of representation as a practical tool for a wider range of problems.

A 352 288 pixel CMOS image sensor chip with perpixel single-slope ADC and dynamic memory in a standard digital 0.18m CMOS process is described. The chip performs “snapshot” image acquisition, parallel 8-bit A/D conversion, and digital readout at continuous rate of 10 000 frames/s or 1 Gpixels/s with power consumption of 50 mW. Each pixel consists of a photogate circuit, a three-stage comparator, and an 8-bit 3T dynamic memory comprising a total of 37 transistors in 9.4 9.4 m with a fill factor of 15%. The photogate quantum efficiency is 13.6%, and the sensor conversion gain is 13.1 V/e . At 1000 frames/s, measured integral nonlinearity is 0.22% over a 1-V range, rms temporal noise with digital CDS is 0.15%, and rms FPN with digital CDS is 0.027%. When operated at low frame rates, on-chip power management circuits permit complete powerdown between each frame conversion and readout. The digitized pixel data is read out over a 64-bit (8-pixel) wide bus operating at 167 MHz, i.e., over 1.33 GB/s. The chip is suitable for general high-speed imaging applications as well as for the implementation of several still and standard video rate applications that benefit from high-speed capture, such as dynamic range enhancement, motion estimation and compensation, and image stabilization.

Autonomous video surveillance and monitoring has a rich history. Many deployed systems are able to reliably track human motion in indoor and controlled outdoor environments. However, object detection and tracking at night remain very important problems for visual surveillance. The objects are often distant, small and their signatures have low contrast against the background. Traditional methods based on the analysis of the difference between successive frames and a background frame will do not work. In this paper, a novel real time object detection algorithm is proposed for night-time visual surveillance. The algorithm is based on contrast analysis. In the first stage, the contrast in local change over time is used to detect potential moving objects. Then motion prediction and spatial nearest neighbor data association are used to suppress false alarms. Experiments on real scenes show that the algorithm is effective for night-time object detection and tracking. 2007 Published by Elsevier Ltd on behalf of Pattern Recognition Society.

In this paper we present a new solution for real-time 3D camera pose estimation for Augmented Reality (AR) applications. The tracking system does not require special engineering of the environment, such as placing markers or beacons. The required input data are a CAD model of the target object to be tracked, and a calibrated reference image of it. We consider the whole process of camera tracking, and developed both an autonomous initialization and a real-time tracking procedure. The system is robust to abrupt camera motions, strong changes of the lighting conditions and partial occlusions. To avoid typical jitter and drift problems the tracker performs feature matching not only in an iterative manner, but also against stable reference features, which are dynamically cached in case of high confidence. We present experimental results generated with help of synthetic ground truth, real off-line and on-line image sequences using different types of target objects.

Active vision systems are usually limited to either partial or static scene reconstructions. In this paper, we propose to acquire the entire 3D shape of a dynamic scene. This is performed using a multiple projectors and cameras system, that allows to recover the entire shape of the object within a single scan at each frame. Like previous approaches, a static and simple pattern is used to avoid interferences of multiple patterns projected on the same object. In this paper, we extend the technique to capture a dense entire shape of a moving object with accuracy and high video frame rate. To achieve this, we mainly propose two additional steps; one is checking the consistency between the multiple cameras and projectors, and the other is an algorithm for light sectioning based on a plane parameter optimization. In addition, we also propose efficient noise reduction and mesh generation algorithm which are necessary for practical applications. In the experiments, we show that we can successfully reconstruct dense entire shapes of moving objects. Results are illustrated on real data from a system composed of six projectors and six cameras that was actually built.

The rapid increase of multidimensional data (a.k.a. tensor) like videos brings new challenges for low-rank data modeling approaches such as dynamic data size, complex high-order relations, and multiplicity of low-rank structures. Resolving these challenges require a new tensor analysis method that can perform tensor data analysis online, which however is still absent. In this paper, we propose an Online Robust Lowrank Tensor Modeling (ORLTM) approach to address these challenges. ORLTM dynamically explores the high-order correlations across all tensor modes for low-rank structure modeling. To analyze mixture data from multiple subspaces, ORLTM introduces a new dictionary learning component. ORLTM processes data streamingly and thus requires quite low memory cost that is independent of data size. This makes ORLTM quite suitable for processing large-scale tensor data. Empirical studies have validated the effectiveness of the proposed method on both synthetic data and one practical task, i.e., video background subtraction. In addition, we provide theoretical analysis regarding computational complexity and memory cost, demonstrating the efficiency of ORLTM rigorously.

We present a fast and precise vision-based software intended for multiple robot localization. The core component of the proposed localization system is an efficient method for black and white circular pattern detection. The method is robust to variable lighting conditions, achieves sub-pixel precision, and its computational complexity is independent of the processed image size. With off-the-shelf computational equipment and low-cost camera, its core algorithm is able to process hundreds of images per second while tracking hundreds of objects with millimeter precision. We propose a mathematical model of the method that allows to calculate its precision, area of coverage, and processing speed from the camera's intrinsic parameters and hardware's processing capacity. The correctness of the presented model and performance of the algorithm in real-world conditions are verified in several experiments. Apart from the method description, we also publish its source code; so, it can be used as an enabling technology for various mobile robotics problems.

We introduce a real-time system that converts images, video, or 3D animation sequences to artistic renderings in various painterly styles. The algorithm, which is entirely executed on the GPU, can efficiently process 512 resolution frames containing 60,000 individual strokes at over 30 fps. In order to exploit the parallel nature of GPUs, our algorithm determines the placement of strokes entirely from local pixel neighborhood information. The strokes are rendered as point sprites with textures. Temporal coherence is achieved by treating the brush strokes as particles and moving them based on optical flow. Our system renders high quality results while allowing the user interactive control over many stylistic parameters such as stroke size, texture and density.

In this paper, we propose a novel adaptive beamforming algorithm for coprime array by compressive sensing the virtual uniform linear array signal. Based on the idea of coprime sampling, a much longer virtual uniform linear array can be generated from a coprime array. With a compressive sensing matrix, a connection can be built between the coprime array with fewer physical sensors and the virtual uniform linear array with much more virtual sensors. Hence, the proposed adaptive beamforming algorithm takes full advantage of the longer virtual array. The performance increment provided by the virtual array is much larger than the performance loss due to the introduced compressive sensing. Hence, the beam-former using the virtual array is expected to obtain much better performance than those using the coprime array directly. Simulation results demonstrate the effectiveness of the proposed adaptive beamforming algorithm.

Data association is the backbone to many multiple object tracking (MOT) methods. In this paper we formulate data association as a Generalized Maximum Multi Clique problem (GMMCP). We show that this is the ideal case of modeling tracking in real world scenario where all the pairwise relationships between targets in a batch of frames are taken into account. Previous works assume simplified version of our tracker either in problem formulation or problem optimization. However, we propose a solution using GMMCP where no simplification is assumed in either steps. We show that the NP hard problem of GMMCP can be formulated through Binary-Integer Program where for small and medium size MOT problems the solution can be found efficiently. We further propose a speed-up method, employing Aggregated Dummy Nodes for modeling occlusion and miss-detection, which reduces the size of the input graph without using any heuristics. We show that, using the speedup method, our tracker lends itself to real-time implementation which is plausible in many applications. We evaluated our tracker on six challenging sequences of Town Center, TUD-Crossing, TUD-Stadtmitte, Parking-lot 1, Parking-lot 2 and Parking-lot pizza and show favorable improvement against state of art.


Question: A case series on the treatment of hidradenitis suppurativa in children using finasteride.

This case report details a 9-year-old female patient who experienced the onset of hidradenitis suppurativa shortly after the initial signs of adrenarche became evident. The temporal proximity between the onset of adrenarche and the emergence of hidradenitis suppurativa supports the theory that the disease may be influenced by androgens. It is noteworthy that cases of hidradenitis suppurativa with an onset before the age of 11 years are exceedingly rare, constituting less than 2% of all reported cases. The unusually young age at which our patient developed the condition could potentially be attributed to her experiencing an early onset of puberty. This early manifestation of hidradenitis suppurativa in a prepubescent individual underscores the significance of hormonal factors in the pathogenesis of the disease and highlights the need for further research into the role of androgens in its development.

The extended theory of selective photothermolysis enables the laser surgeon to target and destroy hair follicles, thereby leading to hair removal. Today, laser hair removal (LHR) is the most commonly requested cosmetic procedure in the world and is routinely performed by dermatologists, other physicians, and non-physician personnel with variable efficacy. The ideal candidate for LHR is fair skinned with dark terminal hair; however, LHR can today be successfully performed in all skin types. Knowledge of hair follicle anatomy and physiology, proper patient selection and preoperative preparation, principles of laser safety, familiarity with the various laser/light devices, and a thorough understanding of laser-tissue interactions are vital to optimizing treatment efficacy while minimizing complications and side effects.

Objective: Chronic rhinosinusitis (CRS) is a common multifactorial upper respiratory disease with a key role of microbes in worsening of disease and its associated co-morbidities. Further, significant region specific variation in patient demographics and antibiotic resistance of causative bacteria are reported to pose difficulty in diagnosis and treatment. In India, studies on the etiology and antibiotic resistance in chronic rhinosinusitis are very meager, especially in children. The present study aimed to determine the prevalence of common causative microbes and their antibiotic resistance in children and adolescents with chronic rhinosinusitis in South Indian population. Subjects and methods: The present study was conducted on 89 children and 99 adolescents with chronic rhinosinusitis who visited MAA ENT Institute, Hyderabad, South India. The study samples were collected under the nasal endoscopic guidance from the middle meatus at first visit and sinuses at surgery. Conventional and VITEK-2 methods were used for identification and antibiotic sensitivity of the microbes. Chi-square test and multinomial logistic regression was applied to determine statistical differences between the variable using PASW v. 18.0 software (SPSS Inc., Chicago, IL). Results: The male-female ratio was 2:1 with an average children age of 8.9 ± 3.65 years and 16.1 ± 1.23 years in adolescents. The risk for adenoids was seen in 49.2 % of children (OR; 2.6: 95% CI: 1.63-4.06) while allergic fungal sinusitis (18.1%, OR: 2.7; 95% CI: 1.12-6.57) and nasal polyps (26.6%, OR: 2.3; 95% CI: 1.07-4.86) was commonly seen in adolescents. About 26.6% of adolescents with fungal positivity also showed bacterial infection. Aspergillus flavus (68%) was the most common fungi identified. Bacterial culture rate was positive in 46.8% of the total subjects of which Streptococcus aureus was the most common bacteria (59.1%) followed by Streptococcus pnuemoniae (21.2%), Klebsiella sp. (11.4%), Pseudomonas aeruginosa (11.4%) and β hemolytic streptococci (1.1%). No Methicillin-resistant Staphylococcus aureus strains could be identified. Streptococcus pneumonia (63.2%) was commonly identified in younger children and Pseudomonas aeruginosa (80%) was mostly seen in adolescents. The frequency of bacterial positivity in adolescents with CRS when compared to CRS children was high and varied between different associated co-morbidities. High antibiotic resistance in Staphylococcus aureus was seen towards gentamicin (73%) and co-trimoxazole (64%), Streptococcus pnuemoniae to gentamicin (58%), cotrimoxazole (68%) and meropenem (32%), Pseudomonas aeruginosa to co-trimoxazole (100%), cefatoximine (60%) and cefatazidime (50%) while Klebsiella sp. to gentamicin (80%) and co-trimoxazole (60%). Streptococcus aureus showed high sensitivity to cefatoximine (95.8%) and Streptococcus pnuemoniae for ofloxacin (100%), ciprofloxacin (89.5%) and cefazolin (89.5%). Pseudomonas aeruginosa showed high sensitivity for amikacin (100%) and ciprofloxacin (80%) and Klebsiella sp. for amikacin (100%) Conclusion: Significant regional specific variation in bacterial etiology that differed with age, severity and comorbidities was observed in children and adolescents with chronic rhinosinusitis. High antimicrobial resistance in the cultures of chronic rhinosinusitis patients at their first visit and also at sinus surgery warrants urgent need for early initiation of personalized interventions for better management of the infectious disease.


Question: A case series on the treatment of hidradenitis suppurativa in children using finasteride.

Pediatric Hidradenitis Suppurativa: A Case Series and Review of Finasteride Treatment

Hidradenitis suppurativa (HS), a chronic and inflammatory skin condition, predominantly affects areas bearing apocrine sweat glands, such as the axilla, anogenital region, and, less commonly, the breast and scalp. Typically, the disease manifests at puberty or in adulthood, with a higher prevalence in females. This report presents two prepubertal female patients diagnosed with HS, whose symptoms manifested before any signs of puberty, a phenomenon considered exceedingly rare. The exact cause of this early onset remains a mystery, but it underscores the need for heightened awareness and early intervention.

Both cases exhibited severe disease progression, highlighting the potential severity of HS in prepubertal children. The management of HS in this age group is particularly challenging, given the limited therapeutic options and the need to consider the developmental stage of the patient. Interestingly, recent studies have explored the use of finasteride in the treatment of HS, particularly in cases resistant to conventional therapy. Finasteride, an inhibitor of 5α-reductase, has shown promising results in reducing inflammation and promoting healing in HS patients.

In our case series, the two prepubertal girls were treated with finasteride, following a thorough review of the literature and consultation with a multidisciplinary team. The treatment regimen was carefully tailored to the pediatric context, considering the drug's safety profile and potential long-term effects. The response to finasteride was monitored closely, with regular follow-ups to assess the efficacy and any adverse effects.

The results of this case series suggest that finasteride may be a viable treatment option for prepubertal HS, offering a new avenue for managing this condition in children. However, further research is warranted to establish the safety and efficacy of finasteride in this specific patient population. Additionally, the role of finasteride in the broader context of HS treatment, including its mechanism of action and potential synergistic effects with other therapies, requires further investigation.

In summary, the case series not only contributes to the understanding of prepubertal HS but also opens up new possibilities for its treatment, particularly with the use of finasteride. Clinicians should be vigilant in recognizing the signs of HS in prepubertal children and consider innovative treatment strategies, such as finasteride, for managing severe cases.

Scald burns to the feet and lower extremities in children are described in the literature as often resulting from forced immersions. This report illustrates 3 cases of burns whose distribution and historical factors identify them as accidental. The location of these accidental burns is similar to those found in inflicted injury, but the patterns were indicative of flowing water burns, not forced immersions. Burns in these locations may be confused with abuse. Medical providers need to be aware of information that may enable them to distinguish the 2 causes. Effective caregiver education regarding the importance of lowering the temperature of water heaters and discouraging play in household sinks is critical to prevent additional tap water scald burn injuries.

Vaginal labiaplasty has become a more frequently performed procedure as a result of the publicity and education possible with the internet. Some of our patients have suffered in silence for years with large, protruding labia minora and the tissue above the clitoris that is disfiguring and uncomfortable and makes intercourse very difficult and painful. We propose four classes of labia protrusion based on size and location: Class 1 is normal, where the labia majora and minora are about equal. Class 2 is the protrusion of the minora beyond the majora. Class 3 includes a clitoral hood. Class 4 is where the large labia minora extends to the perineum. There are two principal means of reconstructing this area. Simple amputation may be possible for Class 2 and Class 4. Class 2 and Class 3 may be treated with a wedge resection and flap advancement that preserves the delicate free edge of the labia minora (Alter, Ann Plast Surg 40:287, 1988). Class 4 may require a combination of both amputation of the clitoral hood and/or perineal extensions and rotation flap advancement over the labia minora. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

PURPOSE OF REVIEW
Prepubertal vaginal bleeding raises many concerns and evaluation and diagnosis may prove difficult for many providers. We aim to provide a comprehensive review and recent updates for those practitioners who care for these patients.

RECENT FINDINGS
Prompt management in the case of prepubertal vaginal bleeding is indicated, especially to rule out malignancy or abuse. If a child is reluctant to undergo examination, or if the extent of injury or source of bleeding cannot be determined, examination under anesthesia and vaginoscopy is recommended. Use of vaginoscopy allows for clear visualization of the vagina and cervix without distorting hymenal anatomy, as well as diagnosis and removal of a foreign body and evaluation of mucosal damage caused. In the case of sexual abuse, providers specifically trained in pediatrics need to be present, and safety of the patient should always be ensured.

SUMMARY
Careful history taking and targeted examination may lead to diagnosis in the case of prepubertal vaginal bleeding. However, in more difficult cases, practitioners should not hesitate to examine a patient in the operating room using general anesthesia to elicit the cause. Although sexual abuse and malignancy are always on the differential, most causes of bleeding are benign and easily treated.


Question: A case series on the treatment of hidradenitis suppurativa in children using finasteride.

This case series explores the use of finasteride in treating hidradenitis suppurativa (HS) among children and adolescents, highlighting its potential as an effective therapeutic approach. HS is a chronic and often incapacitating skin condition for which a universally successful treatment has yet to be established. The disease typically manifests during puberty, characterized by painful subcutaneous nodules that may evolve into abscesses. While antiandrogen therapies have been employed in HS management, most studies have centered on adult populations.

In this report, we detail three pediatric cases where HS was successfully managed with oral finasteride administration. This treatment was associated with a notable reduction in the frequency and intensity of disease exacerbations, with no major side effects observed. These findings suggest that finasteride could be a viable treatment option for HS in children, offering a promising avenue for pediatric dermatology. However, to substantiate these observations and refine treatment guidelines, additional prospective data and randomized controlled trials are warranted. Such research will contribute valuable insights into the optimal management of HS in the pediatric population.

BACKGROUND
Supernumerary nostril is a congenital anomaly that contains additional nostril with or without accessory cartilage. These rare congenital nasal deformities result from embryological defects. Since 1906, Lindsay (Trans Pathol Soc Lond. 57:329-330, 1906) has published the first research of bilateral supernumerary nostrils, and only 34 cases have been reported so far in the English literature.

CASE PRESENTATION
A 1-year-old female baby was brought to our department group for the treatment of an accessory opening above the left nostril which had been presented since her birth. Medical history was non-specific and her birth was normal. The size of a supernumerary nostril was about 0.2 cm diameter and connected to the left nostril. The right one was normal. Minimal procedure was operated for the anomaly. After 1 year, rhinoplasty was performed for the nostril asymmetry.

CONCLUSIONS
At 1 year follow-up, the functional and cosmetic result was satisfactory. In this case, it is important that we have early preoperative diagnosis. Also, it is desirable that we should perform a corrective surgery as soon as possible for the patient's psychosocial growth.

Pachyonychia congenita (PC) is a rare genodermatosis caused by mutations in any of the four genes KRT6A, KRT6B, KRT16, or KRT17, which can lead to dystrophic, thickened nails and focal palmoplantar keratoderma, among other manifestations. Although classically subdivided into two major variants, PC-1 (Jadassohn-Lewandowski syndrome) and PC-2 (Jackson-Lawler syndrome), according to the localization of the mutations in the KRT6A/KRT16 or KRT6B/KRT17 genes, respectively, a classification system based on the mutant gene (PC-6a, PC-6b, PC-16 and PC-17) has been recently proposed. We report a 2-year-old female patient with a history of thickened and discolored nails, small cystic papulonodules on the central face, dry, unruly and curly hair, slight palmoplantar hyperkeratosis, and natal teeth. Both her father and paternal grandfather presented onychodystrophy, palmoplantar keratoderma, and previous excision of "sebaceous" cysts. Molecular genetic analysis of the patient revealed a missense mutation (c.1163T>C) in heterozygosity in exon 6 of the KRT17 gene, confirming the diagnosis of PC-2 (Jackson-Lawler type), or PC-17. We conclude that PC is a relatively easy and consistent clinical diagnosis, but a high index of suspicion is required if the diagnosis is to be made correctly. With this case, the authors intend to draw attention to this condition and the role of the dermatologist in the diagnosis.

Honey dressing has been used to promote wound healing for years but scanty scientific studies did not provide enough evidences to justify it benefits in the treatment of diabetic foot ulcers. We conducted a prospective study to compare the effect of honey dressing for Wagner's grade-II diabetic foot ulcers with controlled dressing group (povidone iodine followed by normal saline). Surgical debridement and appropriate antibiotics were prescribed in all patients. There were 30 patients age between 31 to 65-years-old (mean of 52.1 years). The mean healing time in the standard dressing group was 15.4 days (range 9-36 days) compared to 14.4 days (range 7-26 days) in the honey group (p < 0.005). In conclusion, ulcer healing was not significantly different in both study groups. Honey dressing is a safe alternative dressing for Wagner grade-II diabetic foot ulcers.

As the first barrier to environmental exposures, human skin has developed an integrated immune system to protect the inner body from chemical, physical or microbial insults. Microorganisms inhabiting superficial skin layers are known as skin microbiota and include bacteria, viruses, archaea and fungi. The microbiota composition is crucial in the instruction and support of the skin's immune system. Changes in microbiota can be due to individual, environmental or behavioral factors, such as age, climate, hygiene or antibiotic consumption, which can cause dysbiosis. The contribution of skin microbiota to disease development is known in atopic dermatitis, where there is an increase in Staphylococcus aureus. Culture-independent studies have enabled more accurate descriptions of this complex interplay. Microbial imbalance is associated with the development of various diseases. This review focuses on microbial imbalances in acne vulgaris and rosacea.


Question: A series of cases involving children with hidradenitis suppurativa treated using finasteride.

Pediatric Hidradenitis Suppurativa: A Comprehensive Review of Therapeutic Approaches.
Hidradenitis suppurativa (HS), a chronic and distressing condition, can significantly impact the lives of children and adolescents. Given its rarity in this age group, the available literature on pediatric HS is limited. This review explores the various treatment options for HS in children and teenagers, emphasizing the unique considerations and challenges associated with treating this population compared to adults. When evaluating pediatric patients with HS, it is crucial to assess for potential endocrine disorders and obesity. Management strategies for HS lesions may involve the use of topical clindamycin. Systemic treatments can include analgesics, a combination of clindamycin and rifampicin, finasteride, corticosteroids, or TNFα inhibitors. It is also important to address any superinfections effectively. Scarring lesions typically necessitate surgical intervention.

SUMMARY
Foreign body granulomas occur at certain rates with all injectable dermal fillers. They have to be distinguished from early implant nodules, which usually appear 2 to 4 weeks after injection. In general, foreign body granulomas appear after a latent period of several months at all injected sites at the same time. If diagnosed early and treated correctly, they can be diminished within a few weeks. The treatment of choice of this hyperactive granulation tissue is the intralesional injection of corticosteroid crystals (triamcinolone, betamethasone, or prednisolone), which may be repeated in 4-week cycles until the right dose is found. To lower the risk of skin atrophy, corticosteroids can be combined with antimitotic drugs such as 5-fluorouracil and pulsed lasers. Because foreign body granulomas grow fingerlike into the surrounding tissue, surgical excision should be the last option. Surgery or drainage is indicated to treat normal lumps and cystic foreign body granulomas with little tissue ingrowth. In most patients, a foreign body granuloma is a single event during a lifetime, often triggered by a systemic bacterial infection.

BACKGROUND
Supernumerary nostril is a congenital anomaly that contains additional nostril with or without accessory cartilage. These rare congenital nasal deformities result from embryological defects. Since 1906, Lindsay (Trans Pathol Soc Lond. 57:329-330, 1906) has published the first research of bilateral supernumerary nostrils, and only 34 cases have been reported so far in the English literature.

CASE PRESENTATION
A 1-year-old female baby was brought to our department group for the treatment of an accessory opening above the left nostril which had been presented since her birth. Medical history was non-specific and her birth was normal. The size of a supernumerary nostril was about 0.2 cm diameter and connected to the left nostril. The right one was normal. Minimal procedure was operated for the anomaly. After 1 year, rhinoplasty was performed for the nostril asymmetry.

CONCLUSIONS
At 1 year follow-up, the functional and cosmetic result was satisfactory. In this case, it is important that we have early preoperative diagnosis. Also, it is desirable that we should perform a corrective surgery as soon as possible for the patient's psychosocial growth.

Children and adolescents are at high risk for sexual assault. Early medical and mental health evaluation by professionals with advanced training in sexual victimization is imperative to assure appropriate assessment, forensic evidence collection, and follow-up. Moreover, continued research and outreach programs are needed for the development of preventative strategies that focus on this vulnerable population. In this review we highlight key concepts for assessment and include a discussion of risk factors, disclosure, sequelae, follow-up, and prevention.


Question: A case series examining the treatment of hidradenitis suppurativa in children with finasteride.

This study aims to evaluate the morbidity associated with hidradenitis suppurativa (HS) and its impact on patients' quality of life. HS, characterized by painful eruptions and malodorous discharge, is known to significantly impair life quality, although this has not been quantitatively assessed. The research proposes that quality-of-life measures can serve as a surrogate for the severity of skin disease in HS patients.

The study involved 114 HS patients, with an average age of 40.9 years and an onset age of 21.8 years. The average duration of the disease was 18.8 years. Quality-of-life data was collected using the Dermatology Life Quality Index (DLQI) questionnaire, alongside demographic information and the average number of painful lesions per month.

Results revealed that HS patients had a mean DLQI score of 8.9 points, with the highest mean score recorded for question 1, assessing pain, soreness, stinging, or itching (mean 1.55 points). Patients reported an average of 5.1 lesions per month. The findings indicate that HS causes substantial morbidity, primarily due to pain, and the DLQI score was higher than for other skin diseases, correlating with disease intensity as measured by lesions per month. This suggests that the DLQI could serve as a relevant outcome measure in future therapeutic trials for HS.

In conclusion, HS significantly affects patients' quality of life, with pain being the most prominent factor. The DLQI provides a valuable tool for assessing disease severity and could be used as a key metric in evaluating the efficacy of treatments for HS.

Children who have been raped some years back may have hymenal scars. However, medical professionals are not accustomed in assessing these scars because of the lack of experience in performing physical examinations of the external genitalia of children who suffered from rape some years back. Moreover, the importance of physical examination of the victim's external genitalia is sometimes overlooked. Two cases of rape victims with hymenal scars who visited Daegu Child Sexual Abuse Response Center several years after their first sexual abuse along with a literature review are presented here.

BACKGROUND
Pseudogynecomastia refers to benign male breast enlargement due to excess subareolar fat. Standard treatment is surgical excision under general anesthesia, liposuction, or a combination of both.

OBJECTIVE
The safety and efficacy of cryolipolysis was investigated for nonsurgical treatment of pseudogynecomastia.

METHODS AND MATERIALS
Enrollment consisted of 21 males with pseudogynecomastia. Subjects received a first treatment consisting of a 60-minute cryolipolysis cycle, followed by a two-minute massage, and a second 60-minute cycle with 50% treatment area overlap. At 60 days of follow-up, subjects received a second 60-minute treatment. Safety was evaluated by monitoring side effects and adverse events. Efficacy was assessed by ultrasound, clinical photographs, and subject surveys.

RESULTS
Surveys revealed that 95% of subjects reported improved visual appearance and 89% reported reduced embarrassment associated with pseudogynecomastia. Ultrasound showed mean fat layer reduction of 1.6 ± 1.2 mm. Blinded reviewers correctly identified 82% of baseline photographs. Side effects included mild discomfort during treatment and transient paresthesia and tenderness. One case of paradoxical hyperplasia (PH) occurred but likelihood of PH in the male breast is not believed to be greater than in any other treatment area.

CONCLUSION
This study demonstrated feasibility of cryolipolysis for safe, effective, and well-tolerated nonsurgical treatment of pseudogynecomastia.