Hierarchical Spatio-Temporal Representation Learning for Gait Recognition

• URL: http://arxiv.org/abs/2307.09856v1
• Date: Wed, 19 Jul 2023 09:30:00 GMT
• Title: Hierarchical Spatio-Temporal Representation Learning for Gait Recognition
• Authors: Lei Wang, Bo Liu, Fangfang Liang, Bincheng Wang
• Abstract summary: Gait recognition is a biometric technique that identifies individuals by their unique walking styles. We propose a hierarchical-temporal representation learning framework for extracting gait features from coarse to fine. Our method outperforms the state-of-the-art while maintaining a reasonable balance between model accuracy and complexity.
• Score: 6.877671230651998
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Gait recognition is a biometric technique that identifies individuals by their unique walking styles, which is suitable for unconstrained environments and has a wide range of applications. While current methods focus on exploiting body part-based representations, they often neglect the hierarchical dependencies between local motion patterns. In this paper, we propose a hierarchical spatio-temporal representation learning (HSTL) framework for extracting gait features from coarse to fine. Our framework starts with a hierarchical clustering analysis to recover multi-level body structures from the whole body to local details. Next, an adaptive region-based motion extractor (ARME) is designed to learn region-independent motion features. The proposed HSTL then stacks multiple ARMEs in a top-down manner, with each ARME corresponding to a specific partition level of the hierarchy. An adaptive spatio-temporal pooling (ASTP) module is used to capture gait features at different levels of detail to perform hierarchical feature mapping. Finally, a frame-level temporal aggregation (FTA) module is employed to reduce redundant information in gait sequences through multi-scale temporal downsampling. Extensive experiments on CASIA-B, OUMVLP, GREW, and Gait3D datasets demonstrate that our method outperforms the state-of-the-art while maintaining a reasonable balance between model accuracy and complexity.

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