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Robust visual tracking via speedup multiple kernel ridge regression

[+] Author Affiliations
Cheng Qian

Changzhou Institute of Technology, College of Computer and Information Engineering, Tongjiang South Road, No. 299, Changzhou 213002, China

Durham University, School of Engineering and Computing Sciences, Lower Mountjoy, South Road, Durham DH1 3LE, United Kingdom

Nanjing University of Science and Technology Key Laboratory of Image and Video Understanding for Social Safety, No. 200, Xiaolinwei, Nanjing 210094, China

Toby P. Breckon

Durham University, School of Engineering and Computing Sciences, Lower Mountjoy, South Road, Durham DH1 3LE, United Kingdom

Hui Li

Changzhou Institute of Technology, College of Computer and Information Engineering, Tongjiang South Road, No. 299, Changzhou 213002, China

J. Electron. Imaging. 24(5), 053016 (Sep 21, 2015). doi:10.1117/1.JEI.24.5.053016
History: Received January 24, 2015; Accepted August 20, 2015
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Abstract.  Most of the tracking methods attempt to build up feature spaces to represent the appearance of a target. However, limited by the complex structure of the distribution of features, the feature spaces constructed in a linear manner cannot characterize the nonlinear structure well. We propose an appearance model based on kernel ridge regression for visual tracking. Dense sampling is fulfilled around the target image patches to collect the training samples. In order to obtain a kernel space in favor of describing the target appearance, multiple kernel learning is introduced into the selection of kernels. Under the framework, instead of a single kernel, a linear combination of kernels is learned from the training samples to create a kernel space. Resorting to the circulant property of a kernel matrix, a fast interpolate iterative algorithm is developed to seek coefficients that are assigned to these kernels so as to give an optimal combination. After the regression function is learned, all candidate image patches gathered are taken as the input of the function, and the candidate with the maximal response is regarded as the object image patch. Extensive experimental results demonstrate that the proposed method outperforms other state-of-the-art tracking methods.

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Citation

Cheng Qian ; Toby P. Breckon and Hui Li
"Robust visual tracking via speedup multiple kernel ridge regression", J. Electron. Imaging. 24(5), 053016 (Sep 21, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.5.053016


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