Multi-Person Tracking with Sparse Detection and Continuous Segmentation

This paper presents an integrated framework for mobile street-level tracking of multiple persons. In contrast to classic tracking-by-detection approa- ches, our framework employs an efficient level-set tracker in order to follow indi- vidual pedestrians over time. This low-level tracker is initialized and periodically updated by a pedestrian detector and is kept robust through a series of consis- tency checks. In order to cope with drift and to bridge occlusions, the resulting tracklet outputs are fed to a high-level multi-hypothesis tracker, which performs longer-term data association. This design has the advantage of simplifying short- term data association, resulting in higher-quality tracks that can be maintained even in situations where the pedestrian detector does no longer yield good de- tections. In addition, it requires the pedestrian detector to be active only part of the time, resulting in computational savings. We quantitatively evaluate our ap- proach on several challenging sequences and show that it achieves state-of-the-art performance.