On-line feature enhancement for adaptive object tracking

This paper presents an adaptive tracking algorithm by online features enhancement. To avoid the distraction of the similar background on tracker, Bayes decision rule is applied to calculate the posterior probability of every pixel belonging to the object and generate a set of candidate confidence maps according to the conditional sample densities from object and background on different features. We evaluate the performance of every candidate confidence map using moment of inertia. Then, an optimal confidence map is selected to be fed to Meanshift which is employed to find the location of the object. At last, we update the target model by the confidence map. Experimental validation of the proposed method is performed and presented on challenging image sequences.