Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Subclass Discriminant Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online selection of discriminative features using bayes error rate for visual tracking
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
A survey of appearance models in visual object tracking
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
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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.