Review of person re‐identification 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 lower‐computational 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.

Keywords

• texture information
• local colour
• optimised metrics
• constant metrics
• similarity measures
• dissimilarity measures