To Track or To Detect? An Ensemble Framework for Optimal Selection

  • Xu Yan
  • Xuqing Wu
  • Ioannis A. Kakadiaris
  • Shishir K. Shah
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7576)

Abstract

This paper presents a novel approach for multi-target tracking using an ensemble framework that optimally chooses target tracking results from that of independent trackers and a detector at each time step. The ensemble model is designed to select the best candidate scored by a function integrating detection confidence, appearance affinity, and smoothness constraints imposed using geometry and motion information. Parameters of our association score function are discriminatively trained with a max-margin framework. Optimal selection is achieved through a hierarchical data association step that progressively associates candidates to targets. By introducing a second target classifier and using the ranking score from the pre-trained classifier as the detection confidence measure, we add additional robustness against unreliable detections. The proposed algorithm robustly tracks a large number of moving objects in complex scenes with occlusions. We evaluate our approach on a variety of public datasets and show promising improvements over state-of-the-art methods.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Xu Yan
    • 1
  • Xuqing Wu
    • 1
  • Ioannis A. Kakadiaris
    • 1
  • Shishir K. Shah
    • 1
  1. 1.Department of Computer ScienceUniversity of HoustonHoustonUSA

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