Lidar-based gait analysis in people tracking and 4D visualization

In this paper we introduce a new approach on gait analysis based on data streams of a Rotating Multi Beam (RMB) Lidar sensor. 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, while occlusions or background noise may affects the observation. The proposed algorithms are embedded into an integrated 4D vision and visualization system. Gait features are exploited in two different components of the workflow. First, in the tracking step the collected characteristic gait parameters support as biometric descriptors the re-identification of people, who temporarily leave the field of interest, and re-appear later. Second, in the visualization module, we display moving avatar models which follow in real time the trajectories of the observed pedestrians with synchronized leg movements. The proposed approach is experimentally demonstrated in eight multi-target scenes.