GaitSnippet: Gait Recognition Beyond Unordered Sets and Ordered Sequences

2025-08-11

AI Summary

In gait recognition, unordered set modeling neglects short-term temporal dependencies, while ordered sequence modeling struggles to capture long-range correlations. To address this, we propose the “gait snippet” paradigm, representing human gait as a personalized, multi-scale composition of action snippets—thereby unifying short- and long-range temporal context modeling. Our method comprises two core components: snippet sampling and snippet modeling, leveraging a lightweight 2D convolutional backbone for efficient snippet-level feature extraction and aggregation. This work introduces the snippet concept to gait recognition for the first time, breaking away from the conventional dichotomy of set- versus sequence-based modeling. Evaluated on Gait3D and GREW benchmarks, our approach achieves rank-1 accuracies of 77.5% and 81.7%, respectively, demonstrating strong effectiveness, robustness, and cross-scenario generalization capability.

Technology Category

• Computer Vision: Motion & Tracking
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Overcoming limitations in gait recognition methods
• Capturing multi-scale temporal context in gait
• Improving accuracy with snippet-based modeling

Innovation

• Conceptualizes gait as individualized action snippets
• Incorporates multi-scale temporal context via snippets
• Uses Snippet Sampling and Modeling for recognition