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Globally Optimal Multi-target Tracking on a Hexagonal Lattice

  • Anton Andriyenko
  • Konrad Schindler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6311)

Abstract

We propose a global optimisation approach to multi-target tracking. The method extends recent work which casts tracking as an integer linear program, by discretising the space of target locations. Our main contribution is to show how dynamic models can be integrated in such an approach. The dynamic model, which encodes prior expectations about object motion, has been an important component of tracking systems for a long time, but has recently been dropped to achieve globally optimisable objective functions. We re-introduce it by formulating the optimisation problem such that deviations from the prior can be measured independently for each variable. Furthermore, we propose to sample the location space on a hexagonal lattice to achieve smoother, more accurate trajectories in spite of the discrete setting. Finally, we argue that non-maxima suppression in the measured evidence should be performed during tracking, when the temporal context and the motion prior are available, rather than as a preprocessing step on a per-frame basis. Experiments on five different recent benchmark sequences demonstrate the validity of our approach.

Keywords

Target Location Integer Linear Program Hexagonal Lattice Observation Model Integer Linear Program Formulation 
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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Supplementary material

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Anton Andriyenko
    • 1
  • Konrad Schindler
    • 1
    • 2
  1. 1.Computer Science DepartmentTU Darmstadt 
  2. 2.Photogrammetry and Remote Sensing GroupETH Zürich 

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