Appearance Descriptors for Person Re-identification: a Comprehensive Review

TL;DR

This paper provides a detailed review of appearance descriptor techniques for person re-identification in video surveillance, focusing on body models and features used to recognize individuals across camera networks.

Contribution

It offers a comprehensive categorization and analysis of current methods, serving as a foundational resource for future research in person re-identification.

Findings

Categorizes approaches based on body models and features

Highlights the importance of local and global appearance features

Serves as a structured knowledge base for researchers

Abstract

In video-surveillance, person re-identification is the task of recognising whether an individual has already been observed over a network of cameras. Typically, this is achieved by exploiting the clothing appearance, as classical biometric traits like the face are impractical in real-world video surveillance scenarios. Clothing appearance is represented by means of low-level \textit{local} and/or \textit{global} features of the image, usually extracted according to some part-based body model to treat different body parts (e.g. torso and legs) independently. This paper provides a comprehensive review of current approaches to build appearance descriptors for person re-identification. The most relevant techniques are described in detail, and categorised according to the body models and features used. The aim of this work is to provide a structured body of knowledge and a starting point for researchers willing to conduct novel investigations on this challenging topic.