Survey/review study
A Review of Techniques on Gait-Based Person Re-Identification
Babak Rahi 1,*, Maozhen Li 1, and Man Qi 2
1 Department of Electronics and Computer Engineering, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom
2 The School of Engineering, University of Warwick, Coventry CV4 7AL, United Kingdom
* Correspondence: babak.h.rahi@hotmail.com
Received: 16 October 2022
Accepted: 14 December 2022
Published:
Abstract: Person re-identification at a distance across multiple non-overlapping cameras has been an active research area for years. In the past ten years, short-term Person re-identification techniques have made great strides in accuracy using only appearance features in limited environments. However, massive intra-class variations and inter-class confusion limit their ability to be used in practical applications. Moreover, appearance consistency can only be assumed in a short time span from one camera to the other. Since the holistic appearance will change drastically over days and weeks, the technique, as mentioned above, will be ineffective. Practical applications usually require a long-term solution in which the subject's appearance and clothing might have changed after the elapse of a significant period. Facing these problems, soft biometric features such as Gait has stirred much interest in the past years. Nevertheless, even Gait can vary with illness, ageing and emotional states, walking surfaces, shoe types, clothes types, carried objects (by the subject) and even environment clutters. Therefore, Gait is considered as a temporal cue that could provide biometric motion information. On the other hand, the shape of the human body could be viewed as a spatial signal which can produce valuable information. So extracting discriminative features from both spatial and temporal domains would benefit this research. This article examines the main approaches used in gait analysis for re-identification over the past decade. We identify several relevant dimensions of the problem and provide a taxonomic analysis of current research. We conclude by reviewing the performance levels achievable with current technology and providing a perspective on the most challenging and promising research directions.
