Multi-person tracking with sparse detection and continuous segmentation

  • Authors:

  • Dennis Mitzel;Esther Horbert;Andreas Ess;Bastian Leibe

  • Affiliations:

  • UMIC Research Centre, RWTH Aachen University, Germany;UMIC Research Centre, RWTH Aachen University, Germany;Computer Vision Laboratory, ETH Zurich, Switzerland;UMIC Research Centre, RWTH Aachen University, Germany

  • Venue:
  • ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
  • Year:
  • 2010

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Abstract

This paper presents an integrated framework for mobile street-level tracking of multiple persons. In contrast to classic tracking-by-detection approaches, our framework employs an efficient level-set tracker in order to follow individual pedestrians over time. This low-level tracker is initialized and periodically updated by a pedestrian detector and is kept robust through a series of consistency 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 detections. In addition, it requires the pedestrian detector to be active only part of the time, resulting in computational savings. We quantitatively evaluate our approach on several challenging sequences and show that it achieves state-of-the-art performance.