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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.

Keywords

Optimal Selection Color Histogram Hungarian Algorithm Deformable Part Model Tracking Candidate 
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

  • 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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