Ship tracking method for resisting similar shape information under satellite videos

Abstract

In maritime management, satellite videos with a wide imaging range can provide beneficial data supplements for ground monitoring systems and improve the comprehensive management level. But there are few ships detailed features and a large amount of similar shape information in ship tracking by satellite videos, which lead to tracking drift even false tracking. A ship tracking method is proposed to resisting the interference information of ship-similar shapes. First, the mutual convolution Siamese network is used to calculate the similarity between the object template and the search area to improve the significance of the ship in the feature map. Second, the region proposal network with hierarchical double regression is proposed. Double regression of candidate boxes according to intersection over union (IOU) thresholds can reduce the influence of the non-rigid motion of the water surface on tracking and improve the IOU between candidate boxes and object ships. Finally, the object discrimination model is designed to enhance the screening ability of candidate boxes using deep and shallow classification head fusion and online training. To verify the effectiveness of the proposed method, tests were conducted on Jilin-1 satellite videos and public remote sensing data sets. The results show that, compared with the existing methods, the proposed method can effectively reduce the interference of similar shape information to ship detail features, and the tracking accuracy and success rate are 83.4% and 58.9%, respectively.