Ching. Y Suen

Work place: Centre for Pattern Recognition and Machine Intelligence, Concordia University Montreal, Canada



Research Interests: Programming Language Theory, Data Structures and Algorithms, Image Processing, Image Manipulation, Image Compression, Pattern Recognition, Natural Language Processing


Ching Y. Suen is the Director of CENPARMI and the Concordia Honorary Chair on AI & Pattern Recognition. He received his Ph.D. degree from UBC (Vancouver) and his Master's degree from the University of Hong Kong. He has served as the Chairman of the Department of Computer Science and as the Associate Dean (Research) of the Faculty of Engineering and Computer Science of Concordia University. Dr. Suen is the recipient of numerous awards, including the Gold Medal from the University of Bari (Italy 2012), the IAPR ICDAR Award (2005), the ITAC/NSERC national award (1993), and the "Concordia Lifetime Research Achievement" and "Concordia Fellow" awards (2008 and 1998 respectively), and the "Teaching Excellence Award" given by the Concordia Council of Student Life in 1995. Prof. Suen is a fellow of the IEEE (since 1986), IAPR (1994), and the Academy of Sciences of the Royal Society of Canada (1995). Currently, he is the Emeritus Editor-in-Chief of “Journal of Pattern Recognition”, an Adviser of “Pattern Recognition Letters”, and Editor of 5 other journals like “Expert system with Application”, “Signal, Image and Video Processing”, and Editor of a new book series on “Language Processing and Pattern Recognition”. He is not only the founder of three conferences: ICDAR, IWFHR/ICFHR, and VI, but has also organized numerous international conferences including ICPR, ICDAR, ICFHR, ICCPOL, and as Honorary Chair of numerous international conferences. In 1997, he created the IAPR ICDAR Awards, to honor both young and established outstanding researchers in the field of Document Analysis and Recognition.

Author Articles
Motion-based Vehicle Speed Measurement for Intelligent Transportation Systems

By Ali Tourani Asadollah Shahbahrami Alireza Akoushideh Saeed Khazaee Ching. Y Suen

DOI:, Pub. Date: 8 Apr. 2019

Video-based vehicle speed measurement systems are known as effective applications for Intelligent Transportation Systems (ITS) due to their great development capabilities and low costs. These systems utilize camera outputs to apply video processing techniques and extract the desired information. This paper presents a new vehicle speed measurement approach based on motion detection. Contrary to feature-based methods that need visual features of the vehicles like license-plate or windshield, the proposed method is able to estimate vehicle’s speed by analyzing its motion parameters inside a pre-defined Region of Interest (ROI) with specified dimensions. This capability provides real-time computing and performs better than feature-based approaches. The proposed method consists of three primary modules including vehicle detection, tracking, and speed measurement. Each moving object is detected as it enters the ROI by the means of Mixture-of-Gaussian background subtraction method. Then by applying morphology transforms, the distinct parts of these objects turn into unified filled shapes and some defined filtration functions leave behind only the objects with the highest possibility of being a vehicle. Detected vehicles are then tracked using blob tracking algorithm and their displacement among sequential frames are calculated for final speed measurement module. The outputs of the system include the vehicle’s image, its corresponding speed, and detection time. Experimental results show that the proposed approach has an acceptable accuracy in comparison with current speed measurement systems.

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