Intelligent/autonomous vehicles, such as self-driving cars, intelligent robots and Unmanned Aerial Vehicles (UAVs) must seamlessly interact with humans, e.g., their drivers/operators/pilots or people in their vicinity, whether being obstacles to be avoided (e.g., pedestrians) or targets to be followed and interact with (e.g., when filming a performing athelete). Furthermore,intelligent vehicles and robots have been increasingly employed to assist humans in real-world applications (e.g., for , autonomous transportation, warehouse logistics, or infrastructure inspection)To this end, autonomous vehicles should be equipped with advanced vision systems that allow them to understand and interact with humans in their surrounding environment. This lecture overviews human-centric AI methods that can be utilized to facilitate visual interaction between humans and autonomous vehicles (e.g., through gestures captured by RGB cameras), in order to ensure their safe and successful cooperation in real-world scenarios. Such methods should: a) demonstrate increased visual perception accuracy to understand human visual cues, b) be robust to input data variations, in order to successfully handle illumination/background/scale changes that are typically encountered in real-world scenarios, and c) produce timely predictions to ensure safety, which is a critical aspect of autonomous vehicles' applications. Deep learning and neural networks play an important role towards this end, covering the following topics: a) human pose/posture estimation from RGB images, b) human action/activity recognition recognition from RGB images/skeleton data, and c) gesture recognition from RGB images/skeleton data. Finally, embedded execution is extremely important, as it facilitates vehicle autonomy, e.g., in communication-denied environments. Application areas include driver/operator/pilot activity recognition, gesture-based control of autonomous vehicles, or gesture recognition for traffic management. The lecture will offer an overview of all the above plus other related topics and will stress the related algorithmic aspects. Some issues on embedded CNN computation (e.g., through fast convolution algorithms) will be overviewed as well.
Dr. 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 (AUTH), Greece. Since 1994, he has been a Professor at the Department of Informatics of AUTH and Director of the Artificial Intelligence and Information Analysis (AIIA) lab. He served as a Visiting Professor at several Universities. His current interests are in the areas of computer vision, machine learning, autonomous systems, intelligent digital media, image/video processing, human-centred computing, affective computing, 3D imaging and biomedical imaging. He has published over 920 papers, contributed to 45 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 13 international journals and General or Technical Chair of 5 international conferences. He delivered 98 keynote/invited speeches worldwide. He co-organized 33 conferences and participated in technical committees of 291 conferences. He participated in 73+ R&D projects, primarily funded by the European Union and is/was principal investigator in 43 such projects. He is the coordinator of the Horizon Europe R&D project TEMA, AUTH principal investigator in H2020 R&D projects Aerial Core, AI4Media (one of the 4 H2020 ICT48 AI flagship projects) and Horizon Europe R&D projects AI4Europe, SIMAR. He is chair of the International AI Doctoral Academy (AIDA) He was chair and initiator of the IEEE Autonomous Systems Initiative, he leads a big European H2020 R&D project MULTIDRONE, he has 35200+ citations to his work and h-index 88+. According to Research.com ranked first in Greece and 319 worldwide in the field of Computer Science (2022).
This seminar will be presented in hybrid mode: you can either attend In-Person or Virtually (via this zoom link).