Moving object detection for vehicle tracking in Wide Area Motion Imagery using 4D filtering

Abstract

Most Wide Area Motion Imagery (WAMI) based trackers use motion based cueing for detecting and tracking moving objects. The results are very high false alarm rates in urban environments with tall structures due to parallax effects. This paper proposes an accurate moving object detection method using a precise orthorectification approach for ground stabilization combined with accurate multiview depth maps to reduce the number of false positives induced by parallax effects by 90 percent. Proposed hybrid moving vehicle detection approach for large scale aerial urban imagery is based on fusion of motion detection mask obtained from median-based background subtraction and tall structures height mask provided by image depth map information. Using buildings mask enables us to improve the object level detection accuracy in terms of F-measure by 57 percent from 22.2% to 79.2%.