Adaptive Subclass Discriminant Analysis Color Space Learning for Visual Tracking

摘要

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.