To track or to detect? an ensemble framework for optimal selection

  • Authors:

  • Xu Yan;Xuqing Wu;Ioannis A. Kakadiaris;Shishir K. Shah

  • Affiliations:

  • Department of Computer Science, University of Houston, Houston, TX;Department of Computer Science, University of Houston, Houston, TX;Department of Computer Science, University of Houston, Houston, TX;Department of Computer Science, University of Houston, Houston, TX

  • Venue:
  • ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
  • Year:
  • 2012

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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.