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Short course: Deep Learning and Computer Vision for Drone Imaging and Cinematography
Friday, January 11, 2019
9:00 a.m.-4:00 p.m.
1110 Kim Building
For More Information:
Ania Picard

A special two-day short course on Deep Learning and Computer Vision for Drone Imaging and Cinematography

Sponsored by Aerospace Engineering and the Maryland Robotics Center Space is limited so reserve your spot early. RSVP here:

The course provides an overview of the various computer vision and deep learning problems encountered in drone imaging and cinematography, which is one of the main application areas of drone technologies. The same machine learning and computer vision problems do occur in other drone applications as well, e.g., for land/marine surveillance, search&rescue,  building and machine inspection.

The lectures of Part A (first day, 6 hours) provide a solid background on the necessary topics of computer vision  (Image acquisition, camera geometry, Stereo and Multiview imaging, Motion estimation) and machine learning (Introduction to neural networks, Perceptron, backpropagation, Deep neural networks, Convolutional NNs). It also provides an Introduction to multiple drone imaging.

The lectures of Part B (second day, 6 hours) provide in-depth views of the various topics encountered in multiple drone cinematography, ranging from the definitions of drone audiovisual shooting types (Drone cinematography) to drone mission planning and control, drone localization and mapping, target detection and tracking, imaging for safety (crowd detection, emergency landing, map updating), privacy protection, ethics and regulatory issues.

Part A (8 hours), Sample topic list
Introduction to multiple drone imaging (1 hour) Introduction in computer vision (1 hour) Image acquisition, camera geometry (1 hour) Stereo and Multiview imaging (1 hour) Motion estimation (1 hour) Introduction to neural networks. Perceptron, backpropagation (1 hour) Deep neural networks. Convolutional NNs. (1 hour) Multiple drone architecture and communications (1 hour)

Part B (8 hours) Sample topic list
Drone cinematography  (1 hour)
Drone mission planning  and control (1 hour) Drone HCI issues (1 hour) Localization and mapping (1 hour) Deep learning for target detection (1 hour) Target tracking and 3D localization (1 hour) Imaging for drone safety (1 hour) Drone cinematography mission simulations (1 hour) Privacy protection, ethics and regulatory issues (1 hour) The course content and exact lecture topics may vary from the above ones depending on recent advances and will be finalized in consultation with the local organizer.

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, machine learning, computer vision, intelligent digital media, human centered interfaces, affective computing, 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 28200+ citations to his work and h-index 81+ (Google Scholar). 
Prof. Pitas leads the big European H2020 R&D project MULTIDRONE: He is chair of the Autonomous Systems initiative

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