Target tracking algorithm based on occlusion prediction

Tracking failure caused by occlusion is one of the most challenging problems in visual tracking tasks. The current Siamese network-based object tracking algorithms use the maximum confidence strategy and center cropping mechanism, which may lead to partial or complete loss of target tracking. To address this problem, we propose an occlusion prediction-based tracking method, which uses an occlusion prediction branch to evaluate the occlusion degree of the target in four directions: up, down, left and right, and combines information fusion to predict the target position, and uses dynamic background suppression to reduce the interference of similar targets. First, we use dynamic background suppression to preprocess the input image, weaken the interference around the target, then use the target location information and regression information output by the Siamese network to obtain the candidate target position, and finally use the occlusion prediction information for information fusion to obtain the final target position. Although our method brings additional computational burden, it can still achieve real-time performance. Experiments show that our proposed method can effectively improve the robustness of Siamese tracking algorithms under different occlusion conditions. In the interference test based on OTB100, our method improves the tracking success rate by about 7% and the precision by about 10% for SiamBan and SiamGat algorithms respectively; in the interference test based on VOT2018, our method can effectively reduce the tracking loss frequency of Siamban and SiamGat algorithms, improving the EAO(Expected Average Overlap) by about 10%.