Robust visual tracking using feature-based visual attention

Psychophysical findings have shown that human vision system has an ability to improve target search by enhancing the representation of image components that are related to the searched target, which is the so-called feature-based visual attention. In this paper, motivated by these psychophysical findings, we propose a robust visual tracking algorithm by simulating such feature-based visual attention. Specially, we consider the general sparse basis functions extracted on a large set of natural image patches as features. We define that a feature is related to the target when succeeding activations of that feature cannot increase system´s entropy. The target is finally represented by the probability distribution of those related features. The target search is performed by minimizing the Matusita distance measure between the distributions of the target model and candidate using Newton-style iterations. The experimental results verify that the proposed method is more robust and effective than widely used mean shift based methods.