Disagreement-Based Multi-system Tracking

  • Quannan Li
  • Xinggang Wang
  • Wei Wang
  • Yuan Jiang
  • Zhi-Hua Zhou
  • Zhuowen Tu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7729)


In this paper, we tackle the tracking problem from a fusion angle and propose a disagreement-based approach. While most existing fusion-based tracking algorithms work on different features or parts, our approach can be built on top of nearly any existing tracking systems by exploiting their disagreements. In contrast to assuming multi-view features or different training samples, we utilize existing well-developed tracking algorithms, which themselves demonstrate intrinsic variations due to their design differences. We present encouraging experimental results as well as theoretical justification of our approach. On a set of benchmark videos, large improvements (20% ~40%) over the state-of-the-art techniques have been observed.


Tracking Algorithm Time Stamp Unlabeled Data Appearance Model Visual Tracking 
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

  • Quannan Li
    • 1
  • Xinggang Wang
    • 2
  • Wei Wang
    • 3
  • Yuan Jiang
    • 3
  • Zhi-Hua Zhou
    • 3
  • Zhuowen Tu
    • 1
    • 4
  1. 1.Lab of Neuro ImagingUniversity of CaliforniaLos AngelesUSA
  2. 2.Huazhong University of Science and TechnologyChina
  3. 3.National Key Laboratory for Novel Software TechnologyNanjing UniversityChina
  4. 4.Microsoft Research AsiaChina

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