Multivehicle Object Tracking in Satellite Video Enhanced by Slow Features and Motion Features

With the development of video satellites, multimoving object tracking in satellite video is possible and has become a new challenging task. The difficulties are mainly caused by the characteristics of satellite videos: 1) small objects; 2) low contrast between objects and background; and 3) background in a state of continuous motion. These characteristics make it difficult for the advanced multiobject tracking algorithms in the natural video to give full play to their advantages, resulting in vast false alarms, missed objects, ID switches, and low-confidence bounding boxes. To tackle these problems, a novel multimoving object tracking method considering slow features (SFs) and motion features has been proposed in this research, named SF and motion feature-guided multiobject tracking (SFMFMOT), which realizes the continuous tracking of moving vehicles in satellite videos. A nonmaximum suppression (NMS) module guided by bounding box proposals based on SFs is designed to assist the object detection part by utilizing the sensitivity of SF analysis to the changed pixels. While removing a large number of static false alarms and supplementing missed objects, it improves the recall rate by increasing the confidence score of the correctly detected object bounding boxes. In order to improve the tracking performance, a set of optimization strategies based on motion features and time accumulation information are proposed to smooth the trajectory, remove static false alarms, and duplicate bounding boxes. The proposed method is evaluated in three satellite videos and its superiority is demonstrated.