Satellite Video Tracking by Multi-Feature Correlation Filters with Motion Estimation

As a novel method of earth observation, video satellites can observe dynamic changes in ground targets in real time. To make use of satellite videos, target tracking in satellite videos has received extensive interest. However, this also faces a variety of new challenges such as global occlusion, low resolution, and insufficient information compared with traditional target tracking. To handle the abovementioned problems, a multi-feature correlation filter with motion estimation is proposed. First, we propose a motion estimation algorithm that combines a Kalman filter and an inertial mechanism to alleviate the boundary effects. This can also be used to track the occluded target. Then, we fuse a histogram of oriented gradient (HOG) features and optical flow (OF) features to improve the representation information of the target. Finally, we introduce a disruptor-aware mechanism to weaken the influence of background noise. Experimental results verify that our algorithm can achieve high tracking performance.