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Visual Tracking via Patch-Based Absorbing Markov Chain

  • Ziwei Xiong
  • Nan Zhao
  • Chenglong Li
  • Jin Tang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11004)

Abstract

Bounding box description of target object usually includes background clutter, which easily degrades tracking performance. To handle this problem, we propose a general approach to learn robust object representation for visual tracking. It relies a novel patch-based absorbing Markov chain (AMC) algorithm. First, we represent object bounding box with a graph whose nodes are image patches, and introduce a weight for each patch that describes its reliability belonging to foreground object to mitigate background clutter. Second, we propose a simple yet effective AMC-based method to optimize reliable foreground patch seeds as their qualities are very important for patch weight computation. Third, based on the optimized seeds, we also utilize AMC to compute patch weights. Finally, the patch weights are incorporated into object feature description and tracking is carried out by adopting structured support vector machine algorithm. Experiments on the benchmark dataset demonstrate the effectiveness of our proposed approach.

Keywords

Visual tracking Absorbing Markov chain Weighted patch representation Seed optimization 

Notes

Acknowledgment

This work was jointly supported by National Natural Science Foundation of China (61702002, 61472002), Natural Science Foundation of Anhui Province (1808085QF187), Natural Science Foundation of Anhui Higher Education Institution of China (KJ2017A017) and Co-Innovation Center for Information Supply & Assurance Technology of Anhui University.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyAnhui UniversityHefeiChina

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