Online Transfer Boosting for object tracking

Changxin Gao, Nong Sang, Rui Huang

Abstract: To deal with the drifting issue in visual tracking, we propose an Online Transfer Boosting (OTB) algorithm that transfers knowledge from three different source domains to the target domain to improve the performance of the online classifier used in tracking-by-detection. In particular, the OTB algorithm integrates three types of knowledge by: (1) transferring prior knowledge from the first frame using semi-supervised learning; (2) transferring appearance changes from the previous frames by dynamically updating the learning factor; and (3) transferring observed sample distribution knowledge from the current frame by reweighting the training samples. Experimental results on several public video sequences demonstrated promising performance of OTB in both tracking accuracy and stability.