Abdorreza Joe Afshany

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

E-mail: joe_afshany@msc.guilan.ac.ir


Research Interests: Computer systems and computational processes, Computer Networks, Image Processing


Abdorreza Joe Afshany received his BSc degree in computer software engineering in 2010 and his MSc degree in software engineering at the University of Guilan in 2018. His main research interests include motion detection, image processing, video processing and applying computer vision techniques to practical problems.

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: https://doi.org/10.5815/ijisa.2019.11.03, 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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