Keynote Speakers

Prof. Ioannis Pitas

Department of Informatics,
Aristotle University of Thessaloniki,

IEEE Fellow
IEEE Distinguished Lecturer

Deep Learning and Computer Vision for Multiple Drone Media Production

The aim of drone cinematography is to develop innovative intelligent single- and multiple-drone platforms for media production to cover outdoor events (e.g., sports) that are typically distributed over large expanses, ranging, for example, from a stadium to an entire city. The drone or drone team, to be managed by the production director and his/her production crew, will have: a) increased multiple drone decisional autonomy, hence 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 plays an important role towards this end, covering the following topics: a) drone visual mapping and localization, b) drone visual analysis for target/obstacle/ crowd/POI detection, c) 2D/3D target tracking and d) privacy protection technologies in drones (face de-identification).

This lecture will offer an overview of current research efforts on all related topics, ranging from visual semantic world mapping to multiple drone mission planning and control and to drone perception for autonomous target following, tracking and AV shooting.


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 1090 papers, contributed in 50 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 28600+ 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

Prof. Pierre Moulin

Department of Electrical and Computer Engineering,
University of Illinois,

IEEE Fellow
Editorial Boards,
IEEE Transactions on Information Theory /
IEEE Transactions on Image Processing
Proceedings of IEEE

Forgery Detectors for Adversarial Machine Learning

Deep neural networks achieve state-of-the-art performance for several image classification problems but have been shown to be easily fooled by adversarial perturbations which slightly modify a legitimate image in a specific direction and are visually indistinguishable from the original. This presents a security risk for applications such as autonomous systems. We tackle the problem of detecting such "forgeries" by constructing a locally optimal detector that is well suited to detecting weak signal perturbations. Our general approach is closely related to steganalysis. To illustrate the approach, we present a procedure for learning the forgery detector from a training set, using generative models for image patches. A random ensemble of patches is used for detection of the forgery. The reliability of such detectors is assessed theoretically and experimentally.


Prof. Pierre Moulin received his doctoral degree in 1990, after which he joined at Bell Communications Research as a Research Scientist.

In 1996, he joined the University of Illinois at Urbana-Champaign, where he is currently Professor in the Department of Electrical and Computer Engineering, Research Professor at the Coordinated Science Laboratory and the Beckman Institute and the Coordinated Science Laboratory, and affiliate professor in the Department of Statistics.

His fields of professional interest include statistical decision theory, statistical signal processing and modeling, machine learning, information security, and Shannon theory. Dr. Moulin has served on the editorial boards of the IEEE Transactions on Information Theory, the IEEE Transactions on Image Processing, and the Proceedings of IEEE. He was co-founding Editor-in-Chief of the IEEE Transactions on Information Forensics and Security (2005-2008),member of the IEEE Signal Processing Society Board of Governors (2005-2007), member of the IEEE Information Theory Society Board of Governors (2016-present) and has served IEEE in various other capacities. He is co-recipient of two best paper awards from the IEEE Signal processing Society and was plenary speaker for ICASSP, ICIP, and several other conferences.
He is an IEEE Fellow (2003) and was Distinguished Lecturer of the IEEE Signal Processing Society for 2012-2013 and co-chair of the technical program for ISIT 2015. He was UIUC Sony Faculty Scholar and is the recipient of the 2018 Ronald W. Pratt Faculty Outstanding Teaching Award.

Mr. Pat Hsu

Business Consultant & Head of Enterprise and IIOT Service Unit,
Nokia Networks,

Making 5G Use Case a Commercial Reality

In the mobility space, three major forces will make smart, green and cognitive networks essential over the next five years: 5G, IoT and AI. The intersection of AI, 5G and IoT technologies will bring intelligent connectivity to the world. Nokia is “creating the technology to connect the world”. Soon people will see those vertical applications coming to their daily life once commercial 5G service launch such as:

1. Autonomous driving: AI, connectivity and data will be as important as the vehicle itself, from driving safety to connected autonomous driving.

2. Smart manufacturing: large-scale adoption of robotics and AI-based solutions adds new network requirements.

3. Mobile operators are deploying 5G with AI to create AI-as-a-service or analytics-as-a-service solutions such as video surveillance and analytics for Smart City, Seaport or Airport etc..

During our sharing, the latest 5G technology development, market deployment, ecosystem buildup and commercial ready use cases will be introduced and discussed.


Mr. Pat Hsu re-joined Nokia Networks Greater China in 2014, as the head of Strategy and Business Operations for China Telecom Customer Business Team, he had successfully to expand the China Telecom customer business from multi-million sales per annum in 2014 to global tier 1 customer in 2017, he was relocated to Taipei since 2018 to lead Business Consulting service team and now is head of Enterprise and IIoT Service Unit in Nokia Networks. He is currently working with key partners for 5G Use Cases implementation in Connected Car (Autonomous Driving & C-V2X) deployment and Industry 4.0 ecosystem build up in Taiwan and Greater China regions. Prior to rejoin Nokia Network Greater China, he worked with Accenture to serve Huawei technologies in Next Generation Service Delivery Platform consulting service transformation program as lead SME and Project manager since October, 2013. He spent past 30 years work for HP, Ericsson, Nokia Networks, China ComService and various business development, sales and marketing management roles. He holds M.S. in Computer Science from Florida State University in Tallahassee, Florida USA, and Master of Business Administration (MBA) from Kellogg School of Management, Northwestern University Evanston, Illinois, USA and Hong Kong University of Science & Technology, Hong Kong, China.

Sr. Managing Director Tihao Chiang

Ambarella Taiwan Ltd.

Fellow of IEEE

Ultra HD Computer Vision Processor for Autonomous Driving Applications

Low power computer vision processor has found its wide applications such as sports camera, cell phone, flying camera and automotive camera for ADAS and autonomous driving applications. To achieve a computer vision processor with high quality, ultra HD definition image processing and encoding, it is critical to consider various design parameters such as features, complexity, die size, power while maintaining maximal flexibility for the system designers to innovate and customize for product differentiation. We will describe how to perform trade-off considerations in designing a cost-effective computer vision multimedia processor for mobile and low power applications. We will also discuss the possible applications for such processors.


Tihao Chiang received the Ph.D. degree from Columbia University in 1995. In 1995-1999, he was a program manager at David Sarnoff Research Center (formerly RCA laboratory). In 1999-2008, he was an associate professor at National Chiao-Tung University in Taiwan, R.O.C. He is now with the Ambarella Taiwan Ltd. Dr. Chiang is currently a Fellow of IEEE and holder of over 50 US and worldwide patents. He published over 100 technical journal and conference papers in the field of video and signal processing.