Review of Person Re-identification Techniques

TL;DR

This paper reviews recent person re-identification techniques in video surveillance, discussing various feature extraction methods, similarity measures, and models aimed at improving accuracy and efficiency.

Contribution

It provides a comprehensive summary and comparison of recent developments, highlighting advantages and disadvantages of different approaches in person re-identification.

Findings

Various feature extraction methods are used, including color, texture, and gait.

Different similarity measures and models impact accuracy and computational cost.

The review identifies strengths and limitations of existing techniques.

Abstract

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.