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Visual object tracking challenges revisited: VOT vs. OTB.

Sun Bei1, Zuo Zhen1, Luo Wusheng1

  • 1National University of Defense Technology, Changsha, Hunan, China.

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Summary
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New mirror tracking metrics enhance visual object tracker evaluation by identifying over-fitting. This study reveals OTB ranks are more robust than VOT, improving tracker reliability.

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Area of Science:

  • Computer Vision
  • Machine Learning

Background:

  • Established benchmark datasets and toolkits exist for visual object tracking evaluation.
  • Current evaluation protocols can yield contradictory results, hindering reliable assessment of tracker performance.

Purpose of the Study:

  • Introduce mirror tracking to assess tracker robustness and over-fitting.
  • Evaluate the robustness of evaluation ranks from different protocols.
  • Analyze milestone tracking challenges and their application scenarios.

Main Methods:

  • Proposed mirror tracking metrics for robustness assessment.
  • Experimental validation using OTB and VOT challenges with identical trackers and datasets.
  • Comparative analysis of evaluation protocol rank robustness.

Main Results:

  • Mirror tracking metrics effectively identify tracker over-fitting scenarios.
  • Object Tracking Benchmark (OTB) evaluation ranks demonstrate higher robustness compared to VOT.
  • Combined OTB and VOT ranks provide a measure for failure recovery.

Conclusions:

  • Mirror tracking offers a novel method for evaluating tracker over-fitting.
  • OTB is a more robust evaluation protocol than VOT for visual object tracking.
  • Joint analysis of OTB and VOT ranks enhances the understanding of tracker failure recovery.