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.

1. Introduction

Efforts on analyzing dynamic 3D (i.e. 4D) scenarios with multiple moving pedestrians receive great interest in various application fields, such as intelligent surveillance, video communication and augmented reality. A critical issue is the assignment of broken trajectory segments during the person tracking process, which problem can be caused by frequent occlusions between the people in the scene, or simply by the fact that the pedestrians may temporarily leave the Field of View (F0) and re-appear later. People re-identification [1] needs the extraction of biometric descriptors, which may be weak features in the sense that we should only focus here on a relatively small group of people, instead of identifying someone from a large database. On the other hand, the targets are non-cooperative and they have to be recognized during their natural behavior.