Asadollah Shahbahrami

Work place: Department of Computer Engineering, University of Guilan, Rasht, Iran



Research Interests: Multimedia Information System, Image Processing, Parallel Computing, Systems Architecture, Computer systems and computational processes


Asadollah Shahbahrami received the BSc and MSc degrees in computer engineering (hardware and machine intelligence) from Iran University of Science and Technology and Shiraz University in 1993 and 1996, respectively. He was offered a faculty position in the Department of Electrical Engineering at University of Guilan. He has been working at University of Guilan since August 1996. In January 2004, he joined the Faculty of Electrical Engineering, Mathematics, and Computer Science, Delft University of Technology, Delft, The Netherlands, as a full-time Ph.D. student under advisors Prof. Stamatis Vassiliadis and Dr. Ben Juurlink. He received his PhD degree in September 2008 from Delft University of Technology. He has an assistant professor position in Department of Computer Engineering at the University of Guilan. His research interests include advanced computer architecture, image and video processing, multimedia instructions set design, reconfigurable computing, parallel processing, and SIMD programming.

Author Articles
Parallel Implementation of a Video-based Vehicle Speed Measurement System for Municipal Roadways

By Abdorreza Joe Afshany Ali Tourani Asadollah Shahbahrami Saeed Khazaee Alireza Akoushideh

DOI:, Pub. Date: 8 Nov. 2019

Nowadays, Intelligent Transportation Systems (ITS) are known as powerful solutions for handling traffic-related issues. ITS are used in various applications such as traffic signal control, vehicle counting, and automatic license plate detection. In the special case, video cameras are applied in ITS which can provide useful information after processing their outputs, known as Video-based Intelligent Transportation Systems (V-ITS). Among various applications of V-ITS, automatic vehicle speed measurement is a fast-growing field due to its numerous benefits. In this regard, visual appearance-based methods are common types of video-based speed measurement approaches which suffer from a computationally intensive performance. These methods repeatedly search for special visual features of vehicles, like the license plate, in consecutive frames. In this paper, a parallelized version of an appearance-based speed measurement method is presented which is real-time and requires lower computational costs. To acquire this, data-level parallelism was applied on three computationally intensive modules of the method with low dependencies using NVidia’s CUDA platform. The parallelization process was performed by the distribution of the method’s constituent modules on multiple processing elements, which resulted in better throughputs and massively parallelism. Experimental results have shown that the CUDA-enabled implementation runs about 1.81 times faster than the main sequential approach to calculate each vehicle’s speed. In addition, the parallelized kernels of the mentioned modules provide 21.28, 408.71 and 188.87 speed-up in singularly execution. The reason for performing these experiments was to clarify the vital role of computational cost in developing video-based speed measurement systems for real-time applications.

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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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