Feature selection for Lidar-based gait recognition

In this paper, we present a performance analysis of various descriptors suited to human gait analysis in Rotating Multi-Beam (RMB) Lidar measurement sequences. The gait descriptors for training and recognition are observed and extracted in realistic outdoor surveillance scenarios, where multiple pedestrians walk concurrently in the field of interest, their trajectories often intersect, while occlusions or background noise may affects the observation. For the Lidar scenes, we compared the modifications of five approaches proposed originally for optical cameras or Kinect measurements. Our results confirmed that efficient person re-identification can be achieved using a single Lidar sensor, even if it produces sparse point clouds.