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Self-paced model learning for robust visual tracking

[+] Author Affiliations
Wenhui Huang, Xin Ma, Yibin Li

Shandong University, School of Control Science and Engineering, Jinan, Shandong, China

Jason Gu

Dalhousie University, Department of Electrical and Computer Engineering, Halifax, Nova Scotia, Canada

J. Electron. Imaging. 26(1), 013016 (Feb 22, 2017). doi:10.1117/1.JEI.26.1.013016
History: Received November 4, 2016; Accepted January 26, 2017
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Abstract.  In visual tracking, learning a robust and efficient appearance model is a challenging task. Model learning determines both the strategy and the frequency of model updating, which contains many details that could affect the tracking results. Self-paced learning (SPL) has recently been attracting considerable interest in the fields of machine learning and computer vision. SPL is inspired by the learning principle underlying the cognitive process of humans, whose learning process is generally from easier samples to more complex aspects of a task. We propose a tracking method that integrates the learning paradigm of SPL into visual tracking, so reliable samples can be automatically selected for model learning. In contrast to many existing model learning strategies in visual tracking, we discover the missing link between sample selection and model learning, which are combined into a single objective function in our approach. Sample weights and model parameters can be learned by minimizing this single objective function. Additionally, to solve the real-valued learning weight of samples, an error-tolerant self-paced function that considers the characteristics of visual tracking is proposed. We demonstrate the robustness and efficiency of our tracker on a recent tracking benchmark data set with 50 video sequences.

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Citation

Wenhui Huang ; Jason Gu ; Xin Ma and Yibin Li
"Self-paced model learning for robust visual tracking", J. Electron. Imaging. 26(1), 013016 (Feb 22, 2017). ; http://dx.doi.org/10.1117/1.JEI.26.1.013016


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