GaitCloud: Leveraging Spatial-Temporal Information for LiDAR-Base Gait Recognition with a True-3D Gait Representation

Abstract

Gait recognition using point clouds captured by LiDAR (Light Detection And Ranging) sensors offers better adaptability to variations in walking conditions compared to camera-based methods due to the precise spatial information captured. However existing methods typically project the point clouds into a sequence of 2D depth images extended along the time dimension and adopt gait recognition networks optimized for camera-based approaches. This planar projection compromises the integrity of the 3D coordinates (length width and depth) and results in severe silhouette deformations with varied observation viewpoints similar to the camera-based methods. To better utilize the spatial information in gait point clouds we propose a true 3D gait representation using eff icient point cloud voxelization termed GaitCloud. Additionally we explore the unique nature of LiDAR-captured point clouds and present two improved modules adapted to our method called Layer Encoder (LE) and Horizontal Convolutional Pooling (HCP). Evaluation results using the open-access gait dataset SUSTech1K show that our method outperforms the state-of-the-art achieving recognition accuracies of 93.1% and 89.2% in cross-view and variance experiments respectively. These results demonstrate that 3D gait representation based on point cloud voxelization more effectively utilizes spatial information than depth images offering new possibilities for high-performance LiDAR-based gait recognition. The source code is available at https://github.com/seagrgz/GaitCloud-master.git.