OpenGait: Revisiting Gait Recognition Toward Better Practicality

TL;DR

This paper introduces OpenGait, a comprehensive benchmark and a new baseline model for gait recognition, emphasizing practicality and robustness across indoor and outdoor scenarios, highlighting limitations of prior indoor-focused studies.

Contribution

The paper presents OpenGait, a flexible codebase for gait recognition, and develops GaitBase, a simple yet powerful model that outperforms many existing methods in real-world conditions.

Findings

GaitBase achieves strong performance across multiple datasets.

Indoor dataset conclusions often do not generalize to outdoor scenarios.

OpenGait facilitates comprehensive and practical gait recognition benchmarking.

Abstract

Gait recognition is one of the most critical long-distance identification technologies and increasingly gains popularity in both research and industry communities. Despite the significant progress made in indoor datasets, much evidence shows that gait recognition techniques perform poorly in the wild. More importantly, we also find that some conclusions drawn from indoor datasets cannot be generalized to real applications. Therefore, the primary goal of this paper is to present a comprehensive benchmark study for better practicality rather than only a particular model for better performance. To this end, we first develop a flexible and efficient gait recognition codebase named OpenGait. Based on OpenGait, we deeply revisit the recent development of gait recognition by re-conducting the ablative experiments. Encouragingly,we detect some unperfect parts of certain prior woks, as well as new insights. Inspired by these discoveries, we develop a structurally simple, empirically powerful, and practically robust baseline model, GaitBase. Experimentally, we comprehensively compare GaitBase with many current gait recognition methods on multiple public datasets, and the results reflect that GaitBase achieves significantly strong performance in most cases regardless of indoor or outdoor situations. Code is available at https://github.com/ShiqiYu/OpenGait.