Deep learning assisted visual tracking of evader-UAV

Abstract

In this work the visual tracking of an evading UAV using a pursuer-UAV is examined. The developed method combines principles of deep learning, optical flow, intra-frame homography and correlation based tracking. A Yolo tracker for short term tracking is employed, complimented by optical flow and homography techniques. In case there is no detected evader-UAV, the MOSSE tracking algorithm re-initializes the search and the PTZ-camera zooms-out to cover a wider Filed of View. The camera’s controller adjusts the pan and tilt angles so that the evader-UAV is as close to the center of view as possible, while its zoom is commanded in order to for the captured evader-UAV bounding box cover as much as possible the captured-frame. Experimental studies are offered to highlight the algorithm’s principle and evaluate its performance.