Adaptive Subclass Discriminant Analysis Color Space Learning for Visual Tracking

Authors:
Zhifei Xu;Pengfei Shi;Xiaoyu Xu
Affiliations:
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China 200240;Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China 200240;Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing, China 100080
Venue:
PCM '08 Proceedings of the 9th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Year:
2008

Citing 4
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Subclass Discriminant Analysis

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Online selection of discriminative features using bayes error rate for visual tracking

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A survey of appearance models in visual object tracking

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Abstract

A robust tracking method using subclass discriminant analysis (SDA) color space is presented. SDA color space is proposed which seeks to find the color subspace for representing pixels by maximizing the distance between the foreground pixels and background pixels even if target and background have multi-model color distributions. Further, SDA color space is adaptively updated by only using "confident" target pixels. Experimental results on several challenging videos show the effectiveness of the proposed method.