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.
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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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Xiong, Z., Zhao, N., Li, C., Tang, J. (2018). Visual Tracking via Patch-Based Absorbing Markov Chain. In: Bai, X., Hancock, E., Ho, T., Wilson, R., Biggio, B., Robles-Kelly, A. (eds) Structural, Syntactic, and Statistical Pattern Recognition. S+SSPR 2018. Lecture Notes in Computer Science(), vol 11004. Springer, Cham. https://doi.org/10.1007/978-3-319-97785-0_15
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