Objectives Using satellite video data as a target tracking data source can effectively remedy the problems of fixed angle and a small monitoring range of ground surveillance video, and provide a beneficial supplement for disaster prevention, intelligent transportation, crime tracking and other aspects. However, ground objects in satellite videos are complicated. If the texture, geometry, color and other appearance features of the target are blocked and submerged by other objects, tracking drift or tracking loss will occur in the tracking algorithm.

Methods Based on the fully-convolutional siamese network（SiamFC), this paper proposes an anti-occlusion target tracking algorithm based on satellite video, which can effectively solve the tracking loss problem of moving targets under different cover-up degrees. If the target occlusion degree is low, the forward result obtained by template update network is used as the tracking result of the current frame, and the forward result is set as a new forward template to obtain the changing characteristics of the moving target. If the target is highly obscured, first, the forward result is set as the new backward template, and used to repeatedly track the target in the previous frame. Then, the spatial position prediction module is used to obtain the historical trajectory features of the target and get the trajectory result. Finally, the forward result and the trajectory result are combined to get the tracking result of the current frame based on the bidirectional verification error obtained from the repeated tracking.

Results The proposed algorithm is verified by using typical moving target, aircrafts, ships and vehicles of Jilin-1 satellite video. The results show that the proposed algorithm can provide more accurate tracking results when the target is occluded. The tracking accuracy and success rate reach 76.2% and 78.9%, respectively, and the tracking speed is 33 frame per second.

Conclusions The proposed algorithm can realize the continuous tracking and monitoring of moving targets under occlusion conditions, and can effectively improve the emergency handling ability of intelligent transportation, disaster prevention and relief, wartime monitoring and other aspects in practical application.