Tsoukalas, Athanasios (New York University Abu Dhabi), Xing, Daitao (New York University), Evangeliou, Nikolaos (New York University Abu Dhabi), Giakoumidis, Nikolaos (New York University Abu Dhabi), Tzes, Anthony (New York University Abu Dhabi)

Deep Learning Assisted Visual Tracking of Evader-UAV

Scheduled for presentation during the Regular Session "Learning Methods II" (WeB2), Wednesday, June 16, 2021, 14:00−14:20, Kozani

2021 International Conference on Unmanned Aircraft Systems (ICUAS), June 15-18, 2021, Athens, Greece

This information is tentative and subject to change. Compiled on April 18, 2024

Abstract

IIn 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.