A Novel AMS-DAT Algorithm for Moving Vehicle Detection in a Satellite Video

• Published in: IEEE geoscience and remote sensing letters Vol. 19; pp. 1 - 5
• Main Authors: Chen, Xu, Sui, Haigang, Fang, Jian, Zhou, Mingting, Wu, Chen
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive motion separation (AMS); Algorithms; Detection; difference accumulated trajectory (DAT); Methods; Motion perception; Moving targets; moving vehicle detection; Roads; satellite videos; Satellites; Scale invariance; Separation; Spatiotemporal phenomena; Streaming media; Surveillance; Target detection; Traffic management; Traffic surveillance; Trajectories; Trajectory; Vehicle detection; Vehicles; Video data
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2020.3034677

Satellite videos have recently served as a new data source for a wide range of applications in traffic management and military surveillance. Due to its wider coverage, satellite videos show more advantages in large-scale monitoring than ground surveillance videos. However, pseudomotion background and low-resolution targets pose new challenges to moving vehicle detection in satellite videos, resulting in poor performance of conventional target detection methods when applied to satellite videos. To overcome this difficulty, we propose a novel moving vehicle detection approach using adaptive motion separation and difference accumulated trajectory. Specifically, a new indicator is designed to assist adaptive separation of moving targets and background, considering the scale invariance of vehicles in satellite videos. Meanwhile, we offer a vehicle discrimination algorithm based on a differential accumulated trajectory to distinguish the moving vehicles from the pseudomotion background. Experimental results on two satellite video data sets demonstrate that the proposed approach achieves better detection performance over the state-of-the-art moving vehicle detection methods.