Deep learning and multiple drone vision
Lecturer: Prof. Ioannis Pitas ([email protected])
Aristotle University of Thessaloniki, Greece

Information about the tutorial is also available as a PDF.

Tuesday 27 November 2018
1330-1415 Introduction to Multiple Drone Imaging
1415-1500 Mapping and Localization
1530-1615 Deep Learning for Target Detection
1615-1710 2D Target Tracking and 3D Target Localization

Professor Ioannis Pitas Computer vision pays pivotal role both for drone cinematographic shooting and for drone safety. The aim of drone cinematography is to develop innovative intelligent single- and multiple-drone platforms for media production. Such systems should be able to cover outdoor events (e.g. sports) that are typically distributed over large expanses, ranging, for example, from a stadium to an entire city. In most cases, drone shooting has a target, e.g. cyclists, or boats in case of sports events. Deep learning is currently the principal approach in various computer vision tasks, notably object (shooting target, crowd, landing site) detection. The drone or drone team, to be managed by the production director and his/her production crew, shall have: a) increased multiple drone decisional autonomy for tracking/following the target and allowing event coverage in the time span of around one hour in an outdoor environment and b) improved multiple drone robustness and safety mechanisms (e.g., communication robustness/safety, embedded flight regulation compliance, enhanced crowd avoidance and emergency landing mechanisms), enabling it to carry out its mission against errors or crew inaction and to handle emergencies. Such robustness is particularly important, as the drones will operate close to crowds and/or may face environmental hazards (e.g., wind). Therefore, it must be contextually aware and adaptive, towards maximizing shooting creativity and productivity, while minimizing production costs. Drone vision and machine learning play a very important role towards this end, covering the following topics: a) drone localization, b) drone visual analysis for target/obstacle/crowd/point of interest detection, c) 2D/3D target tracking, d) privacy protection technologies in drones (e.g. face de-identification).

The tutorial will offer an overview of all the above plus other related topics, stressing the algorithmic aspects, such as: a) drone imaging b) drone/target localization and world mapping c) target detection and tracking, d) privacy protection in drones.

Prof. Ioannis Pitas (IEEE fellow, IEEE Distinguished Lecturer, EURASIP fellow) received the Diploma and PhD degree in Electrical Engineering, both from the Aristotle University of Thessaloniki, Greece. Since 1994, he has been a Professor at the Department of Informatics of the same University. He served as a Visiting Professor at several Universities.

His current interests are in the areas of image/video processing, intelligent digital media, machine learning, human centered interfaces, affective computing, computer vision, 3D imaging and biomedical imaging. He has published over 860 papers, contributed in 44 books in his areas of interest and edited or (co-)authored another 11 books. He has also been member of the program committee of many scientific conferences and workshops. In the past he served as Associate Editor or co-Editor of 9 international journals and General or Technical Chair of 4 international conferences. He participated in 69 R&D projects, primarily funded by the European Union and is/was principal investigator/researcher in 41 such projects. He has 28000+ citations (Google Scholar) to his work and h-index 81+ (Google Scholar).

Prof. Pitas leads the big European R&D project MULTIDRONE. He is also chair of the Autonomous Systems initiative.