Rotation Adaptive Correlation Filter for Moving Object Tracking in Satellite Videos

Abstract As a new method of Earth observation, video satellite can provide high-temporal resolution remote sensing images for object tracking. Object tracking in satellite videos is promising yet challenging in computer vision. Although many algorithms for satellite video object tracking have been proposed, none of them solve the problem of tracking rotating object. Due to the nadir view, the rotation of an object is very common in the satellite videos. This problem urgently needs to be addressed. In this paper, a rotation-adaptive correlation filter (RACF) tracking algorithm is proposed to address the problem caused by the rotation of object. The proposed algorithm provides the following improvements: (a) A method of estimating the object rotation angle to keep the feature map stable during the object rotation is proposed. This method can overcome the drawback of histogram of oriented gradient (HOG) based trackers, which cannot deal with the rotation of objects in satellite videos; and (b) making the algorithm capable of estimating the change in the bounding box size caused by object’s rotation. The experimental results demonstrate that our algorithm can track object with a 99.84% precision score and 92.96% success score in six videos from the Jilin-1 satellite constellation.