GaitSnippet: Gait Recognition Beyond Unordered Sets and Ordered Sequences

• URL: http://arxiv.org/abs/2508.07782v1
• Date: Mon, 11 Aug 2025 09:13:38 GMT
• Title: GaitSnippet: Gait Recognition Beyond Unordered Sets and Ordered Sequences
• Abstract summary: We propose a new perspective that conceptualizes human gait as a composition of individualized actions.<n>We introduce a non-trivial solution for snippet-based gait recognition, focusing on Snippet Sampling and Snippet Modeling.<n>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.
• Score: 6.303094638786957
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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