Gait Recognition in Large-scale Free Environment via Single LiDAR

• URL: http://arxiv.org/abs/2211.12371v3
• Date: Tue, 01 Oct 2024 12:36:57 GMT
• Title: Gait Recognition in Large-scale Free Environment via Single LiDAR
• Authors: Xiao Han, Yiming Ren, Peishan Cong, Yujing Sun, Jingya Wang, Lan Xu, Yuexin Ma,
• Abstract summary: LiDAR's ability to capture depth makes it pivotal for robotic perception and holds promise for real-world gait recognition. We present the Hierarchical Multi-representation Feature Interaction Network (HMRNet) for robust gait recognition. To facilitate LiDAR-based gait recognition research, we introduce FreeGait, a comprehensive gait dataset from large-scale, unconstrained settings.
• Score: 35.684257181154905
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human gait recognition is crucial in multimedia, enabling identification through walking patterns without direct interaction, enhancing the integration across various media forms in real-world applications like smart homes, healthcare and non-intrusive security. LiDAR's ability to capture depth makes it pivotal for robotic perception and holds promise for real-world gait recognition. In this paper, based on a single LiDAR, we present the Hierarchical Multi-representation Feature Interaction Network (HMRNet) for robust gait recognition. Prevailing LiDAR-based gait datasets primarily derive from controlled settings with predefined trajectory, remaining a gap with real-world scenarios. To facilitate LiDAR-based gait recognition research, we introduce FreeGait, a comprehensive gait dataset from large-scale, unconstrained settings, enriched with multi-modal and varied 2D/3D data. Notably, our approach achieves state-of-the-art performance on prior dataset (SUSTech1K) and on FreeGait.

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