Anti-occlusion Target tracking algorithm based on satellite video

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, thus providing a beneficial supplement for disaster prevention and relief, intelligent transportation, crime tracking and other aspects. However, ground objects in satellite videos are complicated. When 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: Therefore, based on the SiamFC network, 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. When 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. When the target is highly obscured, firstly, set the forward result to the new backward template, and the backward template is used to repeat the tracking of 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: Taking the video data of the Jilin-1 satellite as an example, the algorithm is verified by using typical moving target aircraft, ships and vehicles. The results show that the proposed algorithm can provide more accurate tracking results when the target is occlusion, and the tracking accuracy and success rate reach 76.2% and 78.9%, respectively. The tracking speed of the proposed algorithm is 33.9 FPS. Conclusions: The 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.