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MEEM: Robust Tracking via Multiple Experts Using Entropy Minimization

  • Jianming Zhang
  • Shugao Ma
  • Stan Sclaroff
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8694)

Abstract

We propose a multi-expert restoration scheme to address the model drift problem in online tracking. In the proposed scheme, a tracker and its historical snapshots constitute an expert ensemble, where the best expert is selected to restore the current tracker when needed based on a minimum entropy criterion, so as to correct undesirable model updates. The base tracker in our formulation exploits an online SVM on a budget algorithm and an explicit feature mapping method for efficient model update and inference. In experiments, our tracking method achieves substantially better overall performance than 32 trackers on a benchmark dataset of 50 video sequences under various evaluation settings. In addition, in experiments with a newly collected dataset of challenging sequences, we show that the proposed multi-expert restoration scheme significantly improves the robustness of our base tracker, especially in scenarios with frequent occlusions and repetitive appearance variations.

Keywords

Image Patch Entropy Minimization Robust Tracking Multiple Instance Learning Multiple Expert 
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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Supplementary material

978-3-319-10599-4_13_MOESM1_ESM.pdf (2.2 mb)
Electronic Supplementary Material (PDF 2,209 KB)

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jianming Zhang
    • 1
  • Shugao Ma
    • 1
  • Stan Sclaroff
    • 1
  1. 1.Department of Computer ScienceBoston UniversityUSA

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