دوره 4، شماره 1 - ( 12-1390 )                   جلد 4 شماره 1 صفحات 29-39 | برگشت به فهرست نسخه ها

XML English Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Asvadi A, Karami M, Baleghi Y. Efficient Object Tracking Using Optimized K-means Segmentation and Radial Basis Function Neural Networks . IJICTR. 2012; 4 (1) :29-39
URL: http://ijict.itrc.ac.ir/article-1-193-fa.html
Efficient Object Tracking Using Optimized K-means Segmentation and Radial Basis Function Neural Networks . نشریه بین المللی فناوری اطلاعات و ارتباطات. 1390; 4 (1) :29-39

URL: http://ijict.itrc.ac.ir/article-1-193-fa.html


چکیده:   (438 مشاهده)

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.

متن کامل [PDF 1588 kb]   (211 دریافت)    
نوع مطالعه: پژوهشي | موضوع مقاله: فناوری اطلاعات

ارسال نظر درباره این مقاله : نام کاربری یا پست الکترونیک شما:
CAPTCHA code

کلیه حقوق این وب سایت متعلق به نشریه بین المللی فناوری اطلاعات و ارتباطات می باشد.

طراحی و برنامه نویسی : یکتاوب افزار شرق

© 2019 All Rights Reserved | International Journal of Information and Communication Technology Research

Designed & Developed by : Yektaweb