Gait recognition, a rapidly advancing vision technology for person identification from a distance, has made significant strides in indoor settings. However, evidence suggests that existing methods often yield unsatisfactory results when applied to newly released real-world gait datasets. Furthermore, conclusions drawn from indoor gait datasets may not easily generalize to outdoor ones. Therefore, the primary goal of this work is to present a comprehensive benchmark study aimed at improving practicality rather than solely focusing on enhancing performance. To this end, we first develop OpenGait, a flexible and efficient gait recognition platform. Using OpenGait as a foundation, we conduct in-depth ablation experiments to revisit recent developments in gait recognition. Surprisingly, we detect some imperfect parts of certain prior methods thereby resulting in several critical yet undiscovered insights. Inspired by these findings, we develop three structurally simple yet empirically powerful and practically robust baseline models, i.e., DeepGaitV2, SkeletonGait, and SkeletonGait++, respectively representing the appearance-based, model-based, and multi-modal methodology for gait pattern description. Beyond achieving SoTA performances, more importantly, our careful exploration sheds new light on the modeling experience of deep gait models, the representational capacity of typical gait modalities, and so on. We hope this work can inspire further research and application of gait recognition towards better practicality. The code is available at https://github.com/ShiqiYu/OpenGait.

翻译：步态识别作为一种通过远距离识别人体的快速发展的视觉技术，已在室内场景取得显著进展。然而，现有证据表明，当将已有方法应用于新发布的真实世界步态数据集时，往往难以获得令人满意的结果。此外，基于室内步态数据集得出的结论可能难以推广至室外场景。因此，本研究的首要目标是提出一项以提升实用性为导向的综合基准研究，而非单纯追求性能优化。为此，我们首先开发了灵活高效的步态识别平台OpenGait。以OpenGait为基础，我们通过深入的消融实验重新审视了步态识别领域的最新进展。令人惊讶的是，我们发现了部分既有方法中存在的缺陷，并由此得出若干关键但尚未被揭示的洞见。基于这些发现，我们构建了三个结构简洁但经验上表现强劲且实践中鲁棒的基线模型，即DeepGaitV2、SkeletonGait和SkeletonGait++，分别代表基于外观、基于模型以及多模态的步态模式描述方法。除实现当前最优性能外，更重要的是，我们的细致探索为深度步态模型的建模经验、典型步态模态的表征能力等方面提供了新视角。期望本研究能推动步态识别技术向更高实用性方向发展，并激发后续研究与应用。代码已开源：https://github.com/ShiqiYu/OpenGait.