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Exploiting Pedestrian Interaction via Global Optimization and Social Behaviors

  • Laura Leal-Taixé
  • Gerard Pons-Moll
  • Bodo Rosenhahn
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7474)

Abstract

Multiple people tracking consists in detecting the subjects at each frame and matching these detections to obtain full trajectories. In semi-crowded environments, pedestrians often occlude each other, making tracking a challenging task. Tracking methods mostly work with the assumption that each pedestrian moves independently unaware of the objects or the other pedestrians around it. In the real world though, it is clear that when walking in a crowd, pedestrians try to avoid collisions, keep a close distance to a group of friends or avoid static obstacles in the scene.

In this paper, we present an approach which includes the interaction between pedestrians in two ways: first, including social and grouping behavior as a physical model within the tracking system, and second, using a global optimization scheme which takes into account all trajectories and all frames to solve the data association problem . Results are presented on three challenging publicly available datasets, showing our method outperforms state-of-the-art tracking systems. We also make a thorough analysis of the effect of the parameters of the proposed tracker as well as its robustness against noise, outliers and missing data.

Keywords

Group Behavior Tracking Accuracy Multiple Object Tracking Track Precision Crowd Simulation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Laura Leal-Taixé
    • 1
  • Gerard Pons-Moll
    • 1
  • Bodo Rosenhahn
    • 1
  1. 1.Leibniz Universität HannoverHannoverGermany

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