Learning Objectness Transfer Networks for Visual Tracking

Shi Pu, Kaili Zhao, Honggang Zhang

Abstract: Existing deep trackers mainly use deep neural networks pre-trained on the object recognition training sets to generate deep features as target representation. However, pre-trained deep features are not effective in representing arbitrary forms of target objects which are likely to be unseen for the pre-trained deep networks. To narrow the gap of representation capability, we propose to transfer the objectness information within pre-trained deep networks. The transferred objectness information is utilized to generate deep features aware of any arbitrary form of target objects for robust visual tracking. Specifically, we design a novel network branch on top of pre-trained deep models to perform incremental transfer learning. The learned network with the transferred objectness information helps to locate target objects undergoing large appearance changes precisely. Experimental results on standard benchmark datasets demonstrate that the proposed algorithm performs favorably against the start-of-the-art trackers.