LidarGait: Benchmarking 3D Gait Recognition with Point Clouds
This work addresses gait recognition for applications in surveillance and biometrics by providing a more robust 3D approach, though it is incremental as it builds on existing gait recognition methods with new sensor data.
The paper tackles the problem of gait recognition in 3D environments by proposing LidarGait, a framework that uses point clouds from LiDAR to extract precise 3D gait features, outperforming existing methods by a significant margin and introducing the SUSTech1K dataset with 25,239 sequences from 1,050 subjects.
Video-based gait recognition has achieved impressive results in constrained scenarios. However, visual cameras neglect human 3D structure information, which limits the feasibility of gait recognition in the 3D wild world. Instead of extracting gait features from images, this work explores precise 3D gait features from point clouds and proposes a simple yet efficient 3D gait recognition framework, termed LidarGait. Our proposed approach projects sparse point clouds into depth maps to learn the representations with 3D geometry information, which outperforms existing point-wise and camera-based methods by a significant margin. Due to the lack of point cloud datasets, we built the first large-scale LiDAR-based gait recognition dataset, SUSTech1K, collected by a LiDAR sensor and an RGB camera. The dataset contains 25,239 sequences from 1,050 subjects and covers many variations, including visibility, views, occlusions, clothing, carrying, and scenes. Extensive experiments show that (1) 3D structure information serves as a significant feature for gait recognition. (2) LidarGait outperforms existing point-based and silhouette-based methods by a significant margin, while it also offers stable cross-view results. (3) The LiDAR sensor is superior to the RGB camera for gait recognition in the outdoor environment. The source code and dataset have been made available at https://lidargait.github.io.