Abstract
Recent advancements in gait recognition have significantly enhanced performance by treating silhouettes as either an unordered set or an ordered sequence. However, both set-based and sequence-based approaches exhibit notable limitations. Specifically, set-based methods tend to overlook short-range temporal context for individual frames, while sequence-based methods struggle to capture long-range temporal dependencies effectively. To address these challenges, we draw inspiration from human identification and propose a new perspective that conceptualizes human gait as a composition of individualized actions. Each action is represented by a series of frames, randomly selected from a continuous segment of the sequence, which we term a snippet. Fundamentally, the collection of snippets for a given sequence enables the incorporation of multi-scale temporal context, facilitating more comprehensive gait feature learning. Moreover, we introduce a non-trivial solution for snippet-based gait recognition, focusing on Snippet Sampling and Snippet Modeling as key components. Extensive experiments on four widely-used gait datasets validate the effectiveness of our proposed approach and, more importantly, highlight the potential of gait snippets. For instance, our method achieves the rank-1 accuracy of 77.5% on Gait3D and 81.7% on GREW using a 2D convolution-based backbone.
Abstract (translated)
近期在步态识别领域的进展显著提升了性能，通过将轮廓视作无序集合或有序序列来处理。然而，基于集的方法和基于序列的方法都存在明显的局限性。具体而言，基于集的方法倾向于忽略单个帧的短时上下文信息，而基于序列的方法则难以有效捕捉长时依赖关系。为了解决这些挑战，我们从人类识别中汲取灵感，并提出了一种新的视角，即将人体步态视为个体化动作组合的概念。每个动作由一系列随机选取自连续片段中的帧表示，我们将这种片段称为“snippet（剪辑）”。从根本上说，针对给定序列收集的片段集合能够整合多尺度的时间上下文信息，从而促进更全面的步态特征学习。 此外，我们还提出了一种基于片段的步态识别非平凡解决方案，着重于片段采样和片段建模作为关键组成部分。在四个广泛使用的步态数据集上进行的大量实验验证了所提方法的有效性，并且更重要的是突显了步态剪辑的应用潜力。例如，在使用2D卷积为基础模型的情况下，我们的方法在Gait3D数据集中达到了77.5%的首位准确率，在GREW数据集中则达到了81.7%的首位准确率。
URL
https://arxiv.org/abs/2508.07782
