People detection and tracking with multiple stereo cameras using particle filters

In this work, a novel approach for people detection and tracking using multiple stereo cameras is proposed. Our proposal consists in combining information from all the available cameras using three different plan-view maps. Occupancy and height maps register the volume and height of the objects that are visible in the stereo cameras, respectively. We also propose the use of a novel map, named confidence map, which registers the confidence of the information projected in each cell. The proposed confidence map is employed to fuse the information captured by each camera so that the most reliable information is kept in each cell. We then propose a particle filter algorithm for tracking people in the fused plan-view maps. The observation model employed considers height, occupancy and confidence information so that information from the most reliable camera is employed at each time instant. The experiments conducted show the validity of our proposal.