OpenGait：面向更好实用性的步态识别综合基准研究
OpenGait: A Comprehensive Benchmark Study for Gait Recognition towards Better Practicality
AI总结：
本文提出综合基准研究平台 OpenGait，通过深入消融实验揭示步态识别关键见解，并开发了 DeepGaitV2、SkeletonGait 和 SkeletonGait++ 三个简单且强大的基线模型，旨在提升步态识别在真实场景中的实用性。
摘要：
步态识别是一种从远处进行人员身份识别的快速发展的视觉技术，在室内环境中取得了重大进展。然而，证据表明，现有方法应用于新发布的真实世界步态数据集时，往往产生令人不满意的结果。此外，从室内步态数据集得出的结论可能不易泛化到室外数据集。因此，本文的主要目标是提出一项旨在提高实用性而不仅仅关注提升性能的综合基准研究。为此，我们开发了 OpenGait，一个灵活高效的步态识别平台。使用 OpenGait，我们进行了深入的消融实验，以重新审视步态识别的最新进展。令人惊讶的是，我们发现了先前某些方法的不完美之处，从而揭示了几个关键但此前被忽视的见解。这些发现促使我们开发了三个结构简单但经验上强大且实际稳健的基线模型：DeepGaitV2、SkeletonGait 和 SkeletonGait++，它们分别代表了用于步态模式描述的基于外观、基于模型和多模态的方法。除了实现最先进的性能外，我们的仔细探索为深度步态模型的建模经验和典型步态模态的表示能力提供了新视角。最后，我们讨论了当前步态识别的关键趋势和挑战，旨在激发迈向更好实用性的进一步发展。代码可在 https://github.com/ShiqiYu/OpenGait 获取。
英文摘要：
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 paper is to present a comprehensive benchmark study aimed at improving practicality rather than solely focusing on enhancing performance. To this end, we developed OpenGait, a flexible and efficient gait recognition platform. Using OpenGait, we conducted in-depth ablation experiments to revisit recent developments in gait recognition. Surprisingly, we detected some imperfect parts of some prior methods and thereby uncovered several critical yet previously neglected insights. These findings led us to develop three structurally simple yet empirically powerful and practically robust baseline models: DeepGaitV2, SkeletonGait, and SkeletonGait++, which represent the appearance-based, model-based, and multi-modal methodologies for gait pattern description, respectively. In addition to achieving state-of-the-art performance, our careful exploration provides new perspectives on the modeling experience of deep gait models and the representational capacity of typical gait modalities. In the end, we discuss the key trends and challenges in current gait recognition, aiming to inspire further advancements towards better practicality. The code is available at https://github.com/ShiqiYu/OpenGait.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
