@inproceedings{c2f259014b3549b1b7f3a29870aba1e2,
title = "Adaptive model meanshift tracking",
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.",
keywords = "Adaptive Clustering, Adaptive Model, LBP Texture Model, MeanShift, Visual Tracking",
author = "Daihou Wang and Changhong Wang and Zhenshen Qu",
year = "2013",
doi = "10.1117/12.2012857",
language = "英语",
isbn = "9780819495884",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
booktitle = "Fifth International Conference on Machine Vision, ICMV 2012",
note = "2012 5th International Conference on Machine Vision: Algorithms, Pattern Recognition and Basic Technologies, ICMV 2012 ; Conference date: 20-10-2012 Through 21-10-2012",
}