Siamese Template Diffusion Networks for Robust Visual Tracking
Yanjie Liang, Penghui Zhao, Yifei Hao, Hanzi Wang · 2022 IEEE International Conference on Multimedia and Expo (ICME) · 2022
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).