Efficient Object Tracking Using Optimized K-means Segmentation and Radial Basis Function Neural Networks

  • Alireza Asvadi Faculty of Electrical & Computer Engineering Babol University of Technology Babol, Iran
  • MohammadReza Karami Faculty of Electrical & Computer Engineering Babol University of Technology Babol, Iran
  • Yasser Baleghi Faculty of Electrical & Computer Engineering Babol University of Technology Babol, Iran
Keywords: computer vision, object tracking, k-means segmentation, radial basis function neural networks, mean shift

Abstract

In this paper, an improved method for object tracking is proposed using Radial Basis Function Neural Networks. Optimized k-means color segmentation is employed for detecting an object in first frame. Next the pixelbased color features (R, G, B) from object is used for representing object color and color features from surrounding background is extracted and extended to develop an extended background model. The object and extended background color features are used to train Radial Basis Function Neural Network. The trained RBFNN is employed to detect object in subsequent frames while mean-shift procedure is used to track object location. The performance of the proposed tracker is tested with many video sequences. The proposed tracker is illustrated to be able to track object and successfully resolve the problems caused by the camera movement, rotation, shape deformation and 3D transformation of the target object. The proposed tracker is suitable for real-time object tracking due to its low computational complexity.

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Author Biographies

Alireza Asvadi, Faculty of Electrical & Computer Engineering Babol University of Technology Babol, Iran

Alireza Asvadi was born in 1984 in Sari (Iran). He received his B.Sc. degree in electrical engineering in 2008 from University of Isfahan, Isfahan, Iran. He is currently working toward the M.Sc. degree in the Department of Electrical & Computer Engineering, Babol (Noushirvani) University of Technology, Babol, Iran. His research interests include the field of computer vision, with an emphasis on object tracking.

MohammadReza Karami, Faculty of Electrical & Computer Engineering Babol University of Technology Babol, Iran

Mohammad Reza Karami received the B.Sc. degree in electronical engineering in 1992, M.Sc. degree in Signal Processing in 1994, and the Ph.D. degree in Biomedical Engineering from I.N.P.L d’Nancy of France. He is now the Associate at the Department of Electrical & Computer Engineering, Babol University of Technology. Since 1998 his research is in signal and speech processing. He has published 80 articles in journals and conferences.

Yasser Baleghi, Faculty of Electrical & Computer Engineering Babol University of Technology Babol, Iran

Yasser Baleghi is an Assistant Professor of Electronic Engineering at Babol University of Technology. He holds a Ph.D. degree in Electronic Engineering from Iran University of Science & Technology. His research interests are evolvable and adaptive hardware, image processing and fault tolerant system design.

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Efficient Object Tracking Using Optimized K-means Segmentation and Radial Basis Function Neural Networks
Published
2011-12-30
How to Cite
Asvadi, A., Karami, M., & Baleghi, Y. (2011, December 30). Efficient Object Tracking Using Optimized K-means Segmentation and Radial Basis Function Neural Networks. International Journal of Information & Communication Technology Research, 4(1), 29-39. Retrieved from http://journal.itrc.ac.ir/index.php/ijictr/article/view/217
Section
Information Technology