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Spatio-Temporal Clustering Model for Multi-object Tracking through Occlusions

  • Lei Zhang
  • Qing Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7726)

Abstract

The occlusion in dynamic or clutter scene is a critical issue in multi-object tracking. Using latent variable to formulate this problem, some methods achieved state-of-the-art performance, while making an exact solution computationally intractable. In this paper, we present a hierarchical association framework to address the problem of occlusion in a complex scene taken by a single camera. At the first stage, reliable tracklets are obtained by frame-to-frame association of detection responses in a flow network. After that, we propose to formulate tracklets association problem in a spatio-temporal clustering model which presents the problem as faithfully as possible. Due to the important role that affinity model plays in our formulation, we then construct a sparsity induced affinity model under the assumption that a detection sample in a tracklet can be efficiently represented by another tracklet belonging to the same object. Furthermore, we give a near-optimal algorithm based on globally greedy strategy to deal with spatio-temporal clustering, which runs linearly with the number of tracklets. We quantitatively evaluate the performance of our method on three challenging data sets and achieve a significant improvement compared to state-of-the-art tracking systems.

Keywords

Greedy Algorithm Pedestrian Detector Association Problem Greedy Solution Clutter Scene 
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 2013

Authors and Affiliations

  • Lei Zhang
    • 1
  • Qing Wang
    • 1
  1. 1.School of Computer Science and EngineeringNorthwestern Polytechnical UniversityXi’anP.R. China

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