Sassan Azadi

Work place: Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran



Research Interests: Engineering, Image Processing, Robotics, Computational Engineering, Computational Science and Engineering


Sassan Azadi was born in Kermanshah, Iran on 1961. He received his B.S. degree in Electrical Engineering from Sharif University in 1985. He, then began M.S. studies in the field of Electrical engineering in Polytechnic University, Brooklyn from 1988 to 1990. Then he studied in the field of Neuroscience in Syracuse University from 1990 to 1992, and got another M.S. degree. He continued his study in Tarbiat Modar University for his PhD degree from 1998 to 2000. Currently, he is an assistant professor in Semnan University, Iran. His research interests are biomedical engineering, control engineering, robotics, and image processing.

Author Articles
Utilizing GVF Active Contours for Real-Time Object Tracking

By Hamed Tirandaz Sassan Azadi

DOI:, Pub. Date: 8 May 2015

In this paper an object tracking system with utilizing optical flow technique, and Gradient Vector Flow (GVF) active contours is presented. Optical flow technique is less sensitive to background structure and does not need to build a model for the background of image so it would need less time to process the image. GVF active snakes have good precision for image segmentation. However, due to the high computational cost, they are not usually applicable. Since precision and time complexity are the most important factors in the image segmentation, several methods have been developed to overcome these problems. In this paper, we, first, recognize the moving object. Then, the object fame with some pixels surrounding to it, was created. Then, this new frame is sent to the GVF filed calculation procedure. Contour initialization is obtained based on the selected pixels. This approach increases the calculation speed, and therefore makes it possible to use the contour for the tracking. The system was built, and tested with a microcomputer. The results show a speed of 10 to 12 frames per second which is considerably suitable for object tracking approaches. 

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