Multi-Person Tracking with Sparse Detection and Continuous Segmentation

Dennis Mitzel, Esther Horbert, Andreas Ess, Bastian Leibe
European Conference on Computer Vision (ECCV'10)

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.

» Show BibTeX

title={Multi-person tracking with sparse detection and continuous segmentation},
author={Mitzel, Dennis and Horbert, Esther and Ess, Andreas and Leibe, Bastian},

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