Review on Human Re-identification with Multiple Cameras

Human re-identification is the core task in most surveillance systems and it is aimed at matching human pairs from different non-overlapping cameras. There are several challenging issues that need to be overcome to achieve reidentification, such as overcoming the variations in viewpoint, pose, image resolution, illumination and occlusion. In this study, we review existing works in human re-identification task. Advantages and limitations of recent works are discussed. At the end, this paper suggests some future research directions for human re-identification

From Point to Set: Extend the Learning of Distance Metrics

Most of the current metric learning methods are proposed for point-to-point distance (PPD) based classification. In many computer vision tasks, however, we need to measure the point-to-set distance (PSD) and even set-to-set distance (SSD) for classification. In this paper, we extend the PPD based Mahalanobis distance metric learning to PSD and SSD based ones, namely point-to-set distance metric learning (PSDML) and set-to-set distance metric learning (SSDML), and solve them under a unified optimization framework. First, we generate positive and negative sample pairs by computing the PSD and SSD between training samples. Then, we characterize each sample pair by its covariance matrix, and propose a covariance kernel based discriminative function. Finally, we tackle the PSDML and SSDML problems by using standard support vector machine solvers, making the metric learning very efficient for multiclass visual classification tasks. Experiments on gender classification, digit recognition, object categorization and face recognition show that the proposed metric learning methods can effectively enhance the performance of PSD and SSD based classification. 1

Person Re-Identification in Distributed Wide-Area Surveillance

Person re-identification (Re-ID) is a fundamental task in automated video surveillance and has been an area of intense research in the past few years. Given an image or video of a person taken from one camera, re-identification is the process of identifying the person from images or videos taken from a different camera. Re-ID is indispensable in establishing consistent labeling across multiple cameras or even within the same camera to re-establish disconnected or lost tracks. Apart from surveillance it has applications in robotics, multimedia, and forensics. Person re-identification is a diffcult problem because of the visual ambiguity and spatio-temporal uncertainty in a person's appearance across different cameras. However, the problem has received significant attention from the computer-vision-research community due to its wide applicability and utility. In this work, we explore the problem of person re-identification for multi-camera tracking, to understand the nature of Re-ID, constraints and conditions under which it is to be addressed and possible solutions to each aspect. We show that Re-ID for multi-camera tracking is inherently an open set Re-ID problem with dynamically evolving gallery and open probe set. We propose multi-feature person models for both single and multi-shot Re-ID with a focus on incorporating unique features suitable for short as well as long period Re-ID. Finally, we adapt a novelty detection technique to address the problem of open set Re-ID. In conclusion we identify the open issues in Re-ID like, long-period Re-ID and scalability along with a discussion on potential directions for further research.Computer Science, Department o