The rapid increase in population has predominantly increased the demand and usage of the motorized vehicles in all areas. This increase in motor vehicular usage has substantially increased the rate of road accidents in the recent decade. Furthermore, injuries, disabilities, and death due to fatal road accidents have been increasing every year despite the safety measures introduced for the public and private transportation system. Congestion of vehicles, a driver under alcohol or drug influence, distracted driving, street racing, faulty design of cars or traffic lights, tailgating, running red lights & stop signs, improper turns and driving in the wrong direction are some of the real causes of accidents across the globe. There are many advanced surveillance systems implemented for road safety, but the prevention of accidents are still being an effective problem. The existing sophisticated vehicles monitored and traffic surveillance system should be used to prevent accidents from occurring. However real-time observations are difficult with an enormous amount of surveillance data running continuously. With the emerging trends in the field of information and computer science, the use of innovative technologies in real-time can be helpful for accident prevention and detection.
Computer vision is the technology that is designed to imitate how the human visual system works. The digital image data from the multiple surveillance systems are acquired in real-time and the data is analyzed and if there are any incidents such as speeding, reckless driving, accidents, etc. it is identified and reported by the system concurrently. Image classification, object detection, object tracking, semantic segmentation, and instance segmentation are some of the computer vision-based techniques with advanced deep learning approaches which can be used in the real-time accident detection and prevention processes.
Similarly, using neural networks many anomalies can be detected in the movement of vehicles using historical data which can be also used in the prevention of accidents. The recent developments in the use of deep learning approaches in visual recognition can be seen as a significant contribution to advanced computer vision research. Moreover, the assistance of computer vision in the surveillance of traffic for accident prevention and detection in real-time would be more significant. The special issue on “Real-time computer vision for accident prevention and detection”
The list of topics that are relevant includes, but it is not limited to, the following:
Theoretical analysis of Computer Vision-based Visual recognition for Fatal Accidents
Unsupervised, Semi-Supervised and Self-Supervised Feature Learning of Transportation Accidents
A Study on Real-time Applications of Computer Vision and Image Analysis in Traffic Congestion
Deep Vision-based Learning for Accident and Traffic Collision Reconstruction
Future of Computer Vision in Road Safety and Intelligent Traffic
Sensors and Early Vision for Post-Accident and Injury Phases
Computer Vision for Fatigue Detection and Management Technologies
Applications of Neural Networks in Transportation Strategy Planning and Instinctive Decision Making
Advanced Visual Learning Methods for Risk-based Accident Prevention
Computer Vision Algorithms and methodologies for Pre-Crash Analysis
Paper Submission Deadline : May 20, 2021
Paper Acceptance Deadline: Mar 20, 2022
Rochester Institute of Technology, United States
Ryerson University, Canada
Official Email: email@example.com; firstname.lastname@example.org
Official Website: https://www.rit.edu/directory/vsavse-vijayalakshmi-saravanan
Dr.Vijayalakshmi Saravanan is currently working as Adjunct Faculty at Golisano College of Computing and Information Sciences, Rochester Institute of Technology, USA and visiting researcher at WINCORE Lab at Ryerson University, Canada. Earlier she was a Postdoctoral Associate at UB (University at Buffalo), The State University of New York, USA and University of Waterloo, Canada under the prestigious “Schlumberger Faculty for the Future” Fellowship award (2015-2017). She completed her Ph.D. under the prestigious Erasmus Mundus EU-Govt Fellowship award at Malardalen University, Sweden as a research exchange student. She has 10 years of teaching experience and has published many technical articles in scholarly international journal and conference. She is serving as technical reviewer and program committee member for reputed conferences & journals such as GHC, SIGCSE and Springer. Her research interests include Power-Aware Processor Design, Big Data, IoT, Computer Architecture, multi-core architecture, s/w and h/w co-design of multi-core, power-performance analysis of multi-core architecture. She is also a lead editor for edited book in CRC Press Taylor and Francis, USA. She is a Senior Member of IEEE & ACM,CSI, Ex-Chair for IEEE-WIE VIT affinity group, India (2009-2015), NPA (National Postdoctoral Association) Annual Meetings committee, Workshop/IIA Co-Chair(2017-2018) Poster committee Co-chair (2018-2019) and a Board member of N2WOMEN(Networking Networking Women). Official Website: https://www.rit.edu/directory/vsavse-vijayalakshmi-saravanan
Dr. Isaac Woungang
Director of DABNEL Lab
Department of Computer Science,
Official Email: email@example.com
Official Website: https://www.ryerson.ca/science/programs/graduate/computerscience/our-faculty/woungang-isaac/
Dr.Isaac Woungang received his Ph.D. degree in Mathematics from the University of South, Toulon & Var, France in 1994. From 1999 to 2002, he worked as software engineer at Nortel Networks, Ottawa, Canada. Since 2002, he has been with Ryerson University, Toronto, Canada, where he is now a Professor of Computer Science. His current research interests include radio resource management in next generation wireless networks, Big Data, IoT, and Cloud computing. Dr.Woungang has published 8 books and over 90 refereed technical articles in scholarly international journals and proceedings of international conferences. He has served as Associate Editor of the Computers and Electrical Engineering (Elsevier), and the International Journal of Communication Systems (Wiley). He has Guest Edited several Special Issues with various reputed journals such as Computer Communications (Elsevier) and Telecommunication Systems (Springer). Since January 2012, He serves as Chair of Computer Chapter, IEEE Toronto Section.
Dr. Alagan Anpalagan
Department of Electrical and Computer Engineering,
Official Email: firstname.lastname@example.org
Official Website: https://www.ee.ryerson.ca/people/Anpalagan.html
Dr. Alagan Anpalagan received the B.A.Sc. M.A.Sc. and Ph.D. degrees in Electrical Engineering from the University of Toronto, Canada. He joined the ELCE Department at Ryerson University in 2001 and was promoted to Full Professor in 2010. He served the department as Graduate Program Director (2004-09) and the Interim EE Program Director (2009-10). During his sabbatical, he was a Visiting Professor at Asian Institute of Technology and Visiting Researcher at Kyoto University. His industrial experience includes working at Bell Mobility, Nortel Networks and IBM Canada. He directs a research group working on radio resource management (RRM) and radio access & networking (RAN) areas within WINCORE Lab. He also served as Editor for the IEEE Communications Surveys & Tutorials and IEEE Communications Letters. He co-edited Design and Deployment of Small Cell Networks, Cambridge University Press (2015), Routing in Opportunistic Networks, Springer (2013), Handbook on Green Information and Communication Systems, Academic Press (2012). He served as IEEE Canada Central Area Chair (2013-15), IEEE Toronto Section Chair (2006-07), ComSoc Toronto Chapter Chair (2004-05), IEEE Canada Professional Activities Committee Chair (2009-11). He is a Registered Professional Engineer in the province of Ontario, IEEE Senior Member and Fellow of Institution of Engineering and Technology. His research interests include Radio resource management, Green communication, Energy harvesting technologies, Small cell and heterogeneous networks, Cognitive and cooperative communication, Machine-to-machine communication, Smart grid and smart homes, Wireless sensor networks, Cross-layer design, Performance modeling, Analysis and optimization.