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

Review of Current Methods for Re-Identification in Computer Vision.

The problem of reidentification of a person in multiple cameras is a hot topic in computer vision research. The issue is with the consistent identification of a person in multiple cameras from different viewpoints and environmental conditions.  Many computer vision researchers have been looking into methods that can improve the reidentification of people for many real-world purposes.  There are new methods each year that expand and explore new concepts and improve the accuracy of reidentification.  This paper will look at current developments and the past tends to find what has been done and what is being done to solve this problem.  This paper will start off by introducing the topic as well as covering the basic concepts of the reidentification problem.  Next, it will cover common datasets that are used in today's research.  Then it will look at evaluation techniques.  Then this paper will start to describe simple techniques that are used followed by the current deep learning techniques.  This paper will cover how these techniques are used, what are some of their weaknesses and their strengths.  It will conclude with an overview of some of the best models and show which models have the most promise and which models should be avoided

Appearance-based people recognition by local dissimilarity representations

ABSTRACT Among the possible applications of computer vision to videosurveillance, person re-identification over a network of camera sensors, using cues related to clothing appearance, is gaining much interest. Re-identification techniques can be used for various tasks, e.g., online tracking of a person, and off-line retrieval of all video sequences containing an individual of interest, whose image is given as a query. Recently, some authors proposed to exploit clothing appearance descriptors also to retrieve video sequences of individuals that match a textual description of clothing (e.g., "person wearing a black t-shirt and white trousers"), instead of an image. We name this task "appearance-based people search". This functionality can be useful, e.g., in forensics investigations, where a textual description can be provided by a witness. In this paper, we present and experimentally evaluate a general method to perform both person re-identification and people search, using any given descriptor of clothing appearance that exploits widely used multiple part/multiple component representations. It is based on turning the considered appearance descriptor into a dissimilarity-based one, through a framework we previously proposed for speeding up person re-identification methods. Our approach allows one to deploy systems able to perform both tasks with the same pipeline and processing stages for constructing descriptors

Review of Person Re-identification Techniques

Person re-identification across different surveillance cameras with disjoint fields of view has become one of the most interesting and challenging subjects in the area of intelligent video surveillance. Although several methods have been developed and proposed, certain limitations and unresolved issues remain. In all of the existing re-identification approaches, feature vectors are extracted from segmented still images or video frames. Different similarity or dissimilarity measures have been applied to these vectors. Some methods have used simple constant metrics, whereas others have utilised models to obtain optimised metrics. Some have created models based on local colour or texture information, and others have built models based on the gait of people. In general, the main objective of all these approaches is to achieve a higher-accuracy rate and lowercomputational costs. This study summarises several developments in recent literature and discusses the various available methods used in person re-identification. Specifically, their advantages and disadvantages are mentioned and compared.Comment: Published 201

Video sequences association for people re-identification across multiple non-overlapping cameras

This paper presents a solution of the appearance-based people reidentification problem in a surveillance system including multiple cameras with different fields of vision.We first utilize different color-based features, combined with several illuminant invariant normalizations in order to characterize the silhouettes in static frames. A graph-based approach which is capable of learning the global structure of the manifold and preserving the properties of the original data in a lower dimensional representation is then introduced to reduce the effective working space and to realize the comparison of the video sequences. The global system was tested on a real data set collected by two cameras installed on board a train. The experimental results show that the combination of color-based features, invariant normalization procedures and the graph-based approach leads to very satisfactory results

Video Sequences Association for People Re-identification across Multiple Non-overlapping Cameras

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