Detecting and tracking moving tiny vehicles from satellite based on spatio-temporal constraints

In recent years, with the development of earth observation technology, satellite video can use optical sensors to obtain continuous images from mobile satellite platforms, providing new data for the detection and tracking of large-scale moving targets. At present, moving target detection algorithms have been widely used in ground surveillance video. However, there are many challenges in applying existing target detection algorithms directly to satellite video due to the low resolution of satellite video, non-appearance texture feature of small target, low signal-to-noise ratio, and nonstationary camera platforms, etc. Therefore, we propose a new satellite video moving target detection and tracking framework for this new type of computer vision task. First, we utilize a tensor data structure to exploit the inner spatial and temporal correlation to extract region of interest for target movement. Then, we designed a recognition strategy based on multi-morphology and motion cues to further identify the correct moving targets from the existing noise. Finally, we associate the target detection results of each frame to achieve multi-target tracking. We manually annotated the video data of Jilin-1 satellite, tested the algorithm under different evaluation criteria, and compared the test results with the most advanced benchmark, which proved the advantages of our framework over the benchmark. In addition, the data set can be downloaded from https://github.com/QingyongHu/VISO.