Online Boosting Tracking with Fragmented Model

Abstract

We propose a novel method combining online boosting and fragment to overcome the drifting problem in on-line boosting tracking. We find that in previous on-line boosting method, the voting weights of the first few selectors are so big that the remainders can not affect the final strong classifier. This problem occurs because the voting weight of selectors are passing globally to adapt to the object variation, but usually only parts of object changes significantly in short time, and the changing part only affect its neighborhood, not the whole target area. So we divide the selector into fragments to get spatial information. The best weak classifier in each selector is combined linearly to get the final strong classifier and then find the location of the object in next frame. Experiments show robustness and generality of the proposed method.