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Journal of Electronic Imaging

Robust visual tracking via speedup multiple kernel ridge regression
Author(s): Cheng Qian; Toby P. Breckon; Hui Li
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Paper 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.

Paper Details

Date Published: 21 September 2015
PDF: 17 pages
J. Electron. Imag. 24(5) 053016 doi: 10.1117/1.JEI.24.5.053016
Published in: Journal of Electronic Imaging Volume 24, Issue 5
Show Author Affiliations
Cheng Qian, Changzhou Institute of Technology (China)
Durham Univ. (United Kingdom)
Nanjing Univ. of Science and Technology (China)
Toby P. Breckon, Durham Univ. (United Kingdom)
Hui Li, Changzhou Institute of Technology (China)

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