Open AccessVisual Object Detection For Autonomous UAV Cinematography
Author(s) -
Fotini Patrona,
Paraskevi Nousi,
Ioannis Mademlis,
Anastasios Tefas,
Ioannis Pitas
Publication year - 2020
Publication title -
proceedings of the northern lights deep learning workshop
Language(s) - English
Resource type - Journals
ISSN - 2703-6928
DOI - 10.7557/18.5099
Subject(s) - cinematography , computer science , crts , shot (pellet) , real time computing , object (grammar) , feature (linguistics) , drone , artificial intelligence , computer vision , computer graphics (images) , art , linguistics , chemistry , philosophy , organic chemistry , visual arts , biology , genetics
The popularization of commercial, battery-powered, camera-equipped, Vertical Take-off and Landing (VTOL) Unmanned Aerial Vehicles (UAVs) during the past decade, has significantly affected aerial video capturing operations in varying domains. UAVs are affordable, agile and flexible, having the ability to access otherwise inaccessible spots. However, their limited resources burden computation cinematography techniques on operating with high accuracy and real-time speed on such devices. State-of-the-art object detectors and feature extractors are, thus, studied in this work, aiming to find a trade-off between performance and speed that will allow UAV exploitation for intelligent cinematography purposes. Experimental evaluation on three newly introduced datasets of rowing boats, cyclists and parkour athletes is performed and evidence is provided that even limited-resource autonomous UAVs can indeed be used for cinematography applications.