Vehicle Tracking on Satellite Video Based on Historical Model
Shili Chen, Taoyang Wang, Hongshuo Wang, Yunming Wang, Jianzhi Hong, Tiancheng Dong, Zhen Li
Abstract
Vehicle tracking on satellite videos poses a challenge for the existing object tracking algorithms due to the few features, object occlusion and similar objects appearance. To improve the performance of the object tracking algorithm, a historical model-based tracker intended for satellite videos is proposed in this study. It updates the tracker by using the historical model of each frame in the video, which contains plenty of object information and background information, so as to improve tracking ability on few-feature objects. Furthermore, a historical model evaluation scheme is designed to obtain reliable historical models, which ensures that the tracker is sensitive to the object in the current frame, thus avoiding the impact caused by changes in object appearance and background. Besides, to solve the drift issue of the tracker caused by object occlusion and the appearance of similar objects, an anti-drift tracker correction scheme is proposed as well. According to the comparative experiments conducted on satellite videos dataset SatSOT, our tracker produces an excellent performance. Moreover, sensitivity analysis, varying criteria comparative experiments and ablation experiments are conducted to demonstrate that the proposed schemes are effective in improving the Precision and Success Rate of the tracker.