Object tracking using discriminative feature selection

This paper presents an approach for evaluating multiple color histograms during object tracking. The method adaptively selects histograms that well distinguish foreground from background. The variance ratio is utilized to measure the separability of object and background and to extract top-ranked discriminative histograms. Experimental results demonstrate how this method adapts to changing appearances of both object undergoing tracking and surrounding background. The advantages and limitations of the particle filter with embedded mechanism of histogram selection are demonstrated in comparisons with the standard CamShift tracker and a combination of CamShift with histogram selection.