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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. [ABSTRACT FROM AUTHOR]

In this paper, a novel YOLO-based detection model with deformable convolution network (DCN-YOLOv5) is developed, which is concerned with the object and key points detection and behavior tracking problem for Oplegnathus punctatus in the ammonia nitrogen environment. The proposed model can adapt to the posture change of the object by deforming the receptive field, which solves the problem of false and missed detection caused by the movement and occlusion. Moreover, a new multi-object multi-category tracking algorithm (MOMC-Tracking) is proposed to track and plot the trajectory and calculate the key behavioral characteristics parameters. In addition, an executable software which integrates the proposed DCN-YOLOv5 model and the MOMC-Tracking algorithm is proposed. Extensive experiments show that compared with the typical YOLO series of algorithms, the proposed model in this paper performs the best with the highest accuracy and the fastest convergence speed, where the mAP @0.5 and mAP @0.5:0.95 of the proposed DCN-YOLOv5 model are 93.71% and 57.45%, which are respectively improved by 1.78% and 24.77% as compared with those obtained by the original YOLOv5 model. [ABSTRACT FROM AUTHOR]