About the Special Issue

======================================
Intelligent devices, such as smart wearables, intelligent vehicles, virtual assistants, and robots, are progressively becoming widespread in many aspects of our daily lives, where effective interaction is increasingly desirable. In such applications, the more information exchanged between the user and the system through multiple modalities, the more versatile, efficient, and natural the interaction becomes. Currently, modern intelligent devices do not consider the user state sufficiently into consideration and thus suffer from lack of personalization and low engagement. Interaction logs and verbal data alone are not adequate for genuinely interpreting human behaviours, and therefore there has been a significant effort to analyse human behaviours from video data. Although significant progress has been made so far, there is still much room for improvement in moving from controlled and acted settings to real-world settings. The key aim of this Special Issue is to bring together cutting-edge research and innovative computer vision techniques applied to human behaviour analysis, from the recognition of gestures and activities to the interpretation of these cues at a higher level for predicting cognitive, social, and emotional states. Special issue topics include, but are not limited to:

• Unsupervised, semi-supervised and supervised learning-based approaches to human behaviour analysis;
• Face, gesture, and body analysis;
• Activity recognition and anticipation;
• Affect and emotion recognition;
• Interactive behaviour analysis, including multiparty interaction, human-computer interaction, human-robot interaction;
• Multimodal behaviour analysis, combining vision with other modalities (e.g., audio, vital signs);
• Societal and ethical considerations of human behaviour analysis, including explainability, bias, fairness, privacy;
• Real-time systems for human behaviour analysis on devices with limited on-board computational power;
• Databases and open-source tools for human behaviour analysis;
• Applications in education, healthcare, smart environments, autonomous driving, or any related field.

Guest Editors
======================================
Dr. Oya Celiktutan, King’s College London, United Kingdom
Dr. Albert Ali Salah, Utrecht University, The Netherlands and Bogazici University, Turkey
Dr. Dongmei Jiang, Northwestern Polytechnical University, China

you are welcomed to register in the  CVML Short e-course on ‘Deep Learning and Computer Vision’,  23–24^th August 2021: http://icarus.csd.auth.gr/cvml-short-course-on-deep-learning-and-computer-vision-for-autonomous-systems-2021/

 

It will take place as a two-day e-course (due to COVID-19 circumstances), hosted by the Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece, providing a series of live lectures delivered through a tele-education platform (Zoom). They will be complemented with on-line video recorded lectures and lecture pdfs, to facilitate international participants having time difference issues and to enable you to study at own pace.  You can also self-assess your knowledge, by filling appropriate questionnaires (one per lecture). You will be provided programming to improve your programming skills. You will also have accesses to tutorial exercises to better your theoretical understanding of selected CVML topics.

This 6^th edition of this course is part of the very successful CVML short course series that took place in the last four years.

 

Course description ‘Deep Learning and Computer Vision’

The short e-course consists of 16 1-hour live lectures organized in two Parts (1 Part per day):

Part A lectures (8 hours) provide an in-depth presentation to autonomous systems imaging and the relevant architectures as well as a solid background on the necessary topics of computer vision (Image acquisition, camera geometry, Stereo and Multiview imaging, Mapping and Localization) and machine learning (Introduction to neural networks, Perceptron, backpropagation, Deep neural networks, Convolutional NNs).

Part B lectures (8 hours) provide in-depth views of the various topics encountered in autonomous systems perception, ranging from vehicle localization and mapping, to Neural SLAM, target detection and tracking. Part B also contains application-oriented lectures on autonomous drones, cars and marine vessels, e.g., drone mission planning for cinematography and related applications (marine surveillance, infrastructure/building inspection, car vision).

 

Course lectures

Part A: (first day, 8 lectures)

1. Introduction to autonomous systems imaging
2. Digital Image and Videos
3. Camera geometry
4. Stereo and Multiview imaging
5. Introduction to Artificial Neural Networks. Perceptron
6. Multilayer perceptron. Backpropagation
7. Deep neural networks. Convolutional NNs
8. Introduction to multiple drone imaging

Part B: (second day, 8 lectures)

1. Simultaneous Localization and Mapping
2. Neural Slam
3. Deep Object Detection
4. 2D Visual Object Tracking
5. Drone mission planning and control
6. Introduction to car vision
7. Introduction to autonomous marine vehicles
8. CVML Software Development Tools

 

Though independent, the attendees of this short e-course will greatly benefit by attending the CVML Short e-course on ‘Computer Vision for Autonomous Systems’ 25-27^th August 2021:

http://icarus.csd.auth.gr/cvml-programming-short-course-and-workshop-on-deep-learning-and-computer-vision-for-autonomous-systems-2021/

 

You can use the following link for course registration:

http://icarus.csd.auth.gr/cvml-short-course-on-deep-learning-and-computer-vision-for-autonomous-systems-2021/

 

Lecture topics, sample lecture ppts and videos, self-assessment questionnaires, programming exercises and tutorial exercises can be found therein.

For questions, please contact: Ioanna Koroni <koroniioanna@csd.auth.gr>

 

The short course is organized by Prof. I. Pitas, IEEE and EURASIP fellow and IEEE distinguished speaker.  He is the coordinator of the EC funded International AI Doctoral Academy (AIDA), that is co-sponsored by all 5 European AI R&D flagship projects (H2020 ICT48). He was initiator and first Chair of the IEEE SPS Autonomous Systems Initiative. He is Director of the Artificial Intelligence and Information analysis Lab (AIIA Lab), Aristotle University of Thessaloniki, Greece. He was Coordinator of the European Horizon2020 R&D project Multidrone. He is ranked 249-top Computer Science and Electronics scientist internationally by Guide2research (2018). He has 33800+ citations to his work and h-index 86+.

 

AUTH is ranked 153/182 internationally in Computer Science/Engineering, respectively, in USNews ranking.

 

Relevant links:

1) Prof. I. Pitas:

https://scholar.google.gr/citations?user=lWmGADwAAAAJ&hl=el

2) Horizon2020 EU funded R&D project Aerial-Core: https://aerial-core.eu/

3) Horizon2020 EU funded R&D project Multidrone: https://multidrone.eu/

4) International AI Doctoral Academy (AIDA): http://www.i-aida.org/

5) Horizon2020 EU funded R&D project AI4Media: https://ai4media.eu/

6) AIIA Lab: https://aiia.csd.auth.gr/

 

Sincerely yours

Prof. I. Pitas

Director of the Artificial Intelligence and Information analysis Lab (AIIA Lab)

Aristotle University of Thessaloniki, Greece