Enhanced Moving Object Detection and Tracking in Remote Sensing Videos

Abstract

With the advancement of remote sensing technology, Moving Object Detection (MOD) is becoming increasingly vital in traffic and aerial monitoring systems. Nonetheless, challenges such as low spatial resolution and complex backgrounds present significant hurdles for MOD technologies. Despite advancements in mitigating these issues, the high incidence of false alarms remains a substantial challenge, severely impeding MOD efficiency. Addressing this issue, this research proposes an object tracking algorithm tailored for remote sensing imagery, which integrates Kalman filters with object matching techniques and applies a set of stringent criteria to accurately identify genuine moving objects. Experimental results indicate that proposed approach reduces False Alarms by 90%, while enhancing Precision and Accuracy by 85% and 65%, respectively.