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Adaptive Object Tracking via Multi-Angle Analysis Collaboration.

Wanli Xue1, Zhiyong Feng2,3, Chao Xu4

  • 1School of Computer Science and Technology, Tianjin University, Tianjin 300350, China. xuewanli@tju.edu.cn.

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|October 26, 2018
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Summary
This summary is machine-generated.

This study introduces a multi-angle analysis collaboration tracking (MACT) framework using reinforcement learning. MACT enhances object tracking performance by analyzing multiple perspectives, improving both speed and accuracy.

Keywords:
collaborationmulti-angle analysismulti-dimensional state–action spacereinforcement learningvisual tracking

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Object tracking research traditionally focuses on mathematical aspects.
  • Analyzing tracking problems from diverse viewpoints is crucial for independent research and practical flexibility.

Purpose of the Study:

  • To introduce a novel tracking framework, multi-angle analysis collaboration tracking (MACT), leveraging multi-dimensional state-action space reinforcement learning.
  • To enhance object tracking by incorporating multi-angle analysis through extensible strategic frameworks.

Main Methods:

  • Developed MACT framework with a basic and an extensible strategic framework (feature selection strategy and movement trend strategy).
  • Employed Q-learning algorithm and epsilon-greedy exploration strategy for training the tracker agent.
  • Utilized a customized rewarding function to promote robust object tracking.

Main Results:

  • MACT framework demonstrated effectiveness through contrast experimental evaluations on the OTB50 benchmark.
  • Significant improvements in both speed and accuracy of object tracking were observed with the MACT tracker.

Conclusions:

  • The proposed MACT framework effectively integrates multi-angle analysis into object tracking.
  • The feature selection and movement trend strategies contribute to enhanced tracking performance, offering a flexible and robust solution.