Fragment-based tracking using online multiple kernel learning

Fragment-based tracking methods have shown its robustness in handling partial occlusion and pose change. In this paper, we propose a novel fragment-based tracking approach using on online multiple kernel learning (MKL) method. An online MKL method for object tracking is implemented by considering temporal continuity explicitly. Instead of directly using multiple features of objects, we employ MKL to make full use of multiple fragments of the object. This can automatically assign different weights to the fragments according to their discriminative power. In addition, for better robustness two kinds of independent features are computed to enrich the representation of patches. We build a classifier for each type of feature and assign them different weights according to their performance on classification. Both qualitative and quantitative evaluations on challenging image sequences demonstrate that the proposed tracking approach performs favorably against several state-of-the-art methods.