Multi-person Tracking-by-Detection Based on Calibrated Multi-camera Systems

In this paper, we present an approach for tackling the problem of automatically detecting and tracking a varying number of people in complex scenes. We follow a robust and fast framework to handle unreliable detections from each camera by extensively making use of multi-camera systems to handle occlusions and ambiguities. Instead of using the detections of each frame directly for tracking, we associate and combine the detections to form so called tracklets. From the triangulation relationship between two views, the 3D trajectory is estimated and back-projected to provide valuable cues for particle filter tracking. Most importantly, a novel motion model considering different velocity cues is proposed for particle filter tracking. Experiments are done on the challenging dataset PETS' 09 to show the benefits of our approach and the integrated multi-camera extensions.