Detecting and Tracking Small and Dense Moving Objects in Satellite Videos: A Benchmark
This work addresses the challenge of insufficient appearance information and lack of high-quality datasets for remote sensing applications, though it is incremental as it builds on existing methods with a new dataset and benchmark.
The authors tackled the problem of moving object detection and tracking in satellite videos by building a large-scale dataset with 1,646,038 instances and 3,711 trajectories, and introduced a motion modeling baseline to improve detection rates and reduce false alarms.
Satellite video cameras can provide continuous observation for a large-scale area, which is important for many remote sensing applications. However, achieving moving object detection and tracking in satellite videos remains challenging due to the insufficient appearance information of objects and lack of high-quality datasets. In this paper, we first build a large-scale satellite video dataset with rich annotations for the task of moving object detection and tracking. This dataset is collected by the Jilin-1 satellite constellation and composed of 47 high-quality videos with 1,646,038 instances of interest for object detection and 3,711 trajectories for object tracking. We then introduce a motion modeling baseline to improve the detection rate and reduce false alarms based on accumulative multi-frame differencing and robust matrix completion. Finally, we establish the first public benchmark for moving object detection and tracking in satellite videos, and extensively evaluate the performance of several representative approaches on our dataset. Comprehensive experimental analyses and insightful conclusions are also provided. The dataset is available at https://github.com/QingyongHu/VISO.