Long-term object tracking based on siamese network

Although the siamese network trackers achieve competitive results both on robustness and accuracy, there is still a need to improve the overall tracking capability. In this paper, we proposed a long-term tracker based on the siamese network. We address the problem of long-term tracking where the target objects undergo significant appearance variation due to heavy deformation, occlusion, abrupt motion, and out-of-view. To tackle those problems, we suggest a multi-template fusion tracking scheme. Moreover, patch template update scheme based on optical flow are proposed to boost the overall tracking performance. The extensive results on object tracking benchmark (OTB2013) show that the proposed algorithm achieve much better performance.