Siamese Template Diffusion Networks for Robust Visual Tracking

In recent years, Siamese trackers have shown great potentials in visual tracking. Most of these trackers fix the template with the initial target representation during on-line tracking and the tracking performance heavily depends on the generalization of template matching network learnt off-line. In this paper, we propose novel Siamese Template Diffusion Networks for on-line adaption of target appearance variations during tracking. To be more specific, we embed new feature aggregation modules (FAMs) into a Siamese network to generate more accurate template and search region for better matching. The new FAMs can establish long-term temporal dependencies of templates at both channel level and spatial level. We plug the new FAMs into two Siamese trackers (i.e., SiamFC and SiamFC++), and propose two Siamese template diffusion trackers (i.e., SiamTDN and SiamTDN++). Experimental results on five challenging datasets show that the proposed trackers outperforms their baselines by a large margin (e.g., 19%/5.6% in terms of the EAO score on VOT2018).