Boosting weak classifiers for visual tracking based on kernel regression

This paper proposes an online learning boosting method based on kernel regression for robust visual tracking. Although much progress has been made in using boosting for tracking, it remains a big challenge to get a robust tracker that is insensitive to illumination change, clutter, object deformation, and occlusion. In this paper, we use a nonlinear version of the recursive least square (RLS) algorithm so as to derive weak classifiers for visual tracking, which performs linear regression in a high-dimensional feature space induced by a Mercer kernel. In order to alleviate the computational burden and increase efficiency, we apply online sparsification to filter samples in feature space. In our boosting framework, adaptive linear weak classifiers are performed, the form of which is modified adaptively to cope with scene changes in every frame. Experimental results demonstrate that our proposed method has advantages in dealing with complex background in visual tracking, and often outperforms the state of the art on the popular datasets.