Multi-Person Tracking from a Moving Platform
We address the problem of vision-based multi-person tracking in busy pedestrian zones using a pair of forward-looking cameras mounted on a mobile platform. Specifically, we are interested in the application of such a system for supporting path planning algorithms in the avoidance of dynamic obstacles. The complexity of the problem calls for an integrated solution, which extracts as much visual information as possible and combines it through cognitive feedback. We propose such an approach, which jointly estimates camera position, stereo depth, object detections, and trajectories based on visual information only. We represent the interplay between these components using a graphical model. For each frame, we first estimate the ground surface together with a set of object detections. Conditioned on these results, we then address object interactions and estimate trajectories. Finally, we employ the tracking results to predict future motion for dynamic objects and fuse this information with a static occupancy map estimated from stereo.
References:
A. Ess, B. Leibe, K. Schindler, and L. Van Gool
"external page Moving Obstacle Detection in Highly Dynamic Scenes",
IEEE International Conference on Robotics and Automation (ICRA'09), 2009, best vision paper award.
A. Ess, B. Leibe, K. Schindler, and L. van Gool
"external page Robust Multi-Person Tracking from a Mobile Platform",
IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 31, No. 10, pp. 1831-1846, 2009