Small Target Tracking in Satellite Videos Using Background Compensation

Through the use of video technology, satellites can detect dynamic targets and analyze their motion characteristics. Target tracking can extract dynamic information about key ground targets for target monitoring and trajectory prediction by satellite video. Tracking algorithms are affected by target motion characteristics, such as velocity and direction, as well as background characteristics, such as illumination changes, occlusion, and background similarities with the target. However, these problems are seldom studied with satellite video cameras. Current algorithms are unsuitable for satellite video because of the poor texture and color features of the target in satellite video. Therefore, in this article, we enhance target tracking for satellite video technology using two aspects: 1) sample training strategy and 2) sample characterization. We establish a filter training mechanism for the target and background to improve the discrimination ability of the tracking algorithm. We then build a target feature model using a Gabor filter to enhance the contrast between the target and background. Moreover, we propose a tracking state evaluation index to avoid tracking drift. Tracking experiments using nine sets of Jilin-1 satellite videos show that the proposed approach can accurately locate a target under weak feature attributes. Therefore, this article contributes to more robust tracking using satellite video technology.