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

Xu Yan, Xuqing Wu, Ioannis A. Kakadiaris, Shishir K. Shah

Research output: Chapter in Book/Report/Conference proceedingConference contribution

31 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationComputer Vision, ECCV 2012 - 12th European Conference on Computer Vision, Proceedings
Number of pages14
EditionPART 5
StatePublished - 2012
Event12th European Conference on Computer Vision, ECCV 2012 - Florence, Italy
Duration: Oct 7 2012Oct 13 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 5
Volume7576 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference12th European Conference on Computer Vision, ECCV 2012

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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