将步态视为片段组合，提升识别精度。
GaitSnippet: Gait Recognition Beyond Unordered Sets and Ordered Sequences
- 用随机采样的帧片段替代整序列或无序集合
- 在Gait3D上达77.5%排名1准确率，GREW上81.7%
- 适合关注时序建模与小样本步态识别的研究者
近期步态识别进展通过将轮廓视作无序集合或有序序列显著提升了性能。然而，集合类方法忽略帧间短程时序上下文，序列类方法难以捕捉长程依赖。受人类识别启发，本文提出将步态视为个性化动作的组合，每个动作由连续片段中随机选取的帧组成，称为片段（snippet）。单个序列的片段集合可融合多尺度时序信息，促进更全面的特征学习。本文提出非平凡的片段识别方案，核心为片段采样与建模。在四个主流数据集上实验验证了方法有效性，尤其凸显片段潜力：使用2D卷积主干网络，在Gait3D上达到77.5%的rank-1准确率，在GREW上达到81.7%。
原文摘要 · Abstract (English)
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.
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