Gait Recognition in Large-scale Free Environment via Single LiDAR
This work addresses the problem of robust gait recognition for applications like smart homes and security by providing a dataset and method for large-scale, free environments, though it is incremental in advancing LiDAR-based approaches.
The paper tackles gait recognition in unconstrained environments by introducing a new dataset, FreeGait, and a method called HMRNet, achieving state-of-the-art performance on both existing and new datasets.
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.