OpenGait: Revisiting Gait Recognition Toward Better Practicality

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.

• The paper introduces the OpenGait codebase and proposes GaitBase, a resilient baseline that bridges indoor experiments with real-world challenges.
• It re-evaluates established methods, revealing that components like MGP and FConv underperform on outdoor datasets such as GREW and Gait3D.
• Experimental results demonstrate that a simple, ResNet-inspired design can achieve superior performance across varied environmental conditions.

An In-Depth Analysis of OpenGait: Revisiting Gait Recognition for Enhanced Practicality

The paper "OpenGait: Revisiting Gait Recognition Toward Better Practicality" addresses critical limitations in the domain of gait recognition by assessing prior methodologies and providing improved solutions for real-world applications. This work is particularly significant as gait recognition offers unique advantages for identity authentication in uncontrolled settings, such as public security scenarios.

Gait recognition's current landscape heavily leans on indoor datasets like CASIA-B and OU-MVLP, which may not fully capture the intricacies encountered in outdoor environments. This paper posits that conclusions derived from such datasets bear limited applicability to real-world contexts, thereby necessitating a comprehensive benchmark study.

Key Contributions

1. OpenGait Codebase: The authors introduce OpenGait, a robust codebase crafted to facilitate gait recognition research. It supports diverse datasets and methodologies, easing the implementation of new techniques and ensuring consistent evaluations across state-of-the-art methods. Notably, OpenGait's platform has powered advances in significant international competitions, indicating its practical utility.
2. Comprehensive Re-evaluation: Strikingly, the paper reveals that several widely accepted insights from prior works are not consistent across new outdoor datasets like GREW and Gait3D. For example, components such as the Multi-layer Global Pipeline (MGP) and Focal Convolution Layer (FConv) do not prove superior in real-world settings. This revelation is crucial for directing future research towards methods that maintain robust performance irrespective of environmental changes.
3. Development of GaitBase: Inspired by insightful findings, the paper proposes GaitBase—a structurally simple yet powerful baseline model. GaitBase leverages a ResNet-inspired backbone and demonstrates superior performance across both indoor and outdoor datasets. The paper’s empirical results highlight GaitBase's robustness, particularly in wild conditions, marking it as a significant improvement over existing methods.

Experimental Insights

The experimental analyses yield critical insights into model robustness. One of the notable findings is the ineffectiveness of local feature extraction components when applied to non-laboratory datasets. This discrepancy underscores the necessity for adaptable models well-suited for varied operational conditions. The study also challenges the traditional reliance on fixed datasets, urging researchers to integrate more diverse, real-world data in their experiments.

A strong focus on implementing practical data augmentation strategies further differentiates this paper. By simulating real-world occlusions and transformations, it enhances the model's ability to generalize across different scenarios, which is pivotal for practical deployment.

Implications for Future Research

The findings outlined propose several avenues for future work. The paper recognizes the challenge in achieving seamless gait verification under dynamic conditions, advocating for more robust algorithms capable of executing accurate recognition in diverse settings. Additionally, the suggestion to explore unsupervised learning for general gait representation emphasizes the need to expand beyond labeled datasets, leveraging vast amounts of unannotated information.

Another crucial aspect pointed out is the potential shift to transformer-based models, given their success in other vision tasks. Exploring such architectures could further refine gait recognition systems, optimizing them for the particularly noisy and complex data characteristic of outdoor environments.

Conclusion

This paper significantly enriches the field by meticulously revisiting traditional gait recognition approaches and steering the research towards more resilient, practical solutions. The introduction of the OpenGait codebase and the development of the GaitBase model mark a meaningful step in bridging the gap between laboratory results and real-world applicability. As the vision community continues to evolve, these contributions offer a strong foundation for developing more advanced and practical gait recognition methods.