Improved Multi-Person Tracking with Active Occlusion Handling

We address the problem of vision-based multi-person tracking in busy inner-city locations using a stereo rig mounted on a mobile platform. Specifically, we are interested in the application of such a system for autonomous navigation and path planning. In such a scenario, semantic information about the moving scene objects becomes important. In order to estimate this robustly, we combine classical geometric world mapping with multi-person detection and tracking. In this paper, we refine an approach presented in earlier work, which jointly estimates camera position, stereo depth, object detections, and trajectories based only on visual information. We analyze the influence of the trajectory generator, which forms part of any tracking-by-detection system, and propose a set of measures to improve its performance. The extensions are experimentally evaluated on challenging, realistic video sequences recorded at busy inner-city locations. The results show that the proposed extensions significantly improve overall system performance, making the resulting detecting and tracking capabilities an interesting component of future navigation system for highly dynamic scenes.