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.

翻译：步态识别是最关键的远距离身份识别技术之一，在科研和工业领域日益受到关注。尽管在室内数据集上取得了显著进展，但大量证据表明，步态识别技术在现实场景中表现欠佳。更重要的是，我们发现部分基于室内数据集得出的结论无法推广至实际应用。因此，本文的首要目标是提出一项全面的基准研究以提升实用性，而非仅针对特定模型追求更高性能。为此，我们首先开发了一套灵活高效的步态识别代码库OpenGait。基于OpenGait，我们通过重新进行消融实验，深入回顾了步态识别的最新进展。令人鼓舞的是，我们发现了部分先前研究中存在的不足，并获得了新的见解。受这些发现的启发，我们设计了一个结构简单、经验有效且实际鲁棒的基线模型GaitBase。通过实验，我们将GaitBase与多种当前步态识别方法在多个公开数据集上进行了全面比较，结果表明无论室内还是室外场景，GaitBase在大多数情况下均展现出显著优越的性能。代码可从https://github.com/ShiqiYu/OpenGait获取。