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Proceedings Paper

Adaptive model MeanShift tracking
Author(s): Daihou Wang; Changhong Wang; Zhenshen Qu
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Paper Abstract

Performance of original color-based MeanShift tracking algorithm decreases drastically under variant illumination environment. To enhance the robustness of the tracking ability under variant illumination environment, an adaptive model MeanShift tracking scheme is proposed in this paper. The statistically approximate LBP texture information is adaptively integrated into the model description to increase the descriptive ability of the model under different illuminating condition. The weighted coefficient of the color information and texture information adjust according to the discriminative ability of the character. Besides, H(Hue) element Gaussian model is introduced for more precisely decription as well as reducing the computational cost of the original histogram-based color model. Experiments on video sequences show the proposed model scheme and advance MeanShift tracking algorithm give effective and robust results in variant illumination condition.

Paper Details

Date Published: 13 March 2013
PDF: 5 pages
Proc. SPIE 8784, Fifth International Conference on Machine Vision (ICMV 2012): Algorithms, Pattern Recognition, and Basic Technologies, 878403 (13 March 2013); doi: 10.1117/12.2012857
Show Author Affiliations
Daihou Wang, Rutgers, The State Univ. of New Jersey (United States)
Changhong Wang, Harbin Institute of Technology (China)
Zhenshen Qu, Harbin Institute of Technology (China)


Published in SPIE Proceedings Vol. 8784:
Fifth International Conference on Machine Vision (ICMV 2012): Algorithms, Pattern Recognition, and Basic Technologies
Yulin Wang; Liansheng Tan; Jianhong Zhou, Editor(s)

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