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Robust visual tracking via speedup multiple kernel ridge regression.

Qian, Cheng and Breckon, Toby P. and Li, Hui (2015) 'Robust visual tracking via speedup multiple kernel ridge regression.', Journal of electronic imaging., 24 (5). 053016.


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.

Item Type:Article
Full text:(AM) Accepted Manuscript
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Publisher statement:Copyright 2015 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic electronic or print reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Date accepted:20 August 2015
Date deposited:05 October 2015
Date of first online publication:21 September 2015
Date first made open access:No date available

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