@inproceedings{
han2024gait,
title={Gait Recognition in Large-scale Free Environment via Single Li{DAR}},
author={Xiao Han and Yiming Ren and Peishan Cong and Yujing Sun and Jingya Wang and Lan Xu and Yuexin Ma},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=j3mY13CNrb}
}
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. Code and dataset will be released upon publication of this paper.
Gait Recognition in Large-scale Free Environment via Single LiDAR
Xiao Han Yiming Ren Peishan Cong Yujing Sun Jingya Wang Lan Xu Yuexin Ma