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Kernel Based Object Tracking Using Mean Shift Method

Published on March 2012 by Swati P. Baviskar, Nitin S. Ujgare
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International Conference in Computational Intelligence
Foundation of Computer Science USA
ICCIA - Number 1
March 2012
Authors: Swati P. Baviskar, Nitin S. Ujgare
1c32477d-b35f-4bfe-9da6-9b89138c9def

Swati P. Baviskar, Nitin S. Ujgare . Kernel Based Object Tracking Using Mean Shift Method. International Conference in Computational Intelligence. ICCIA, 1 (March 2012), 21-24.

@article{
author = { Swati P. Baviskar, Nitin S. Ujgare },
title = { Kernel Based Object Tracking Using Mean Shift Method },
journal = { International Conference in Computational Intelligence },
issue_date = { March 2012 },
volume = { ICCIA },
number = { 1 },
month = { March },
year = { 2012 },
issn = 0975-8887,
pages = { 21-24 },
numpages = 4,
url = { /proceedings/iccia/number1/5093-1005/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference in Computational Intelligence
%A Swati P. Baviskar
%A Nitin S. Ujgare
%T Kernel Based Object Tracking Using Mean Shift Method
%J International Conference in Computational Intelligence
%@ 0975-8887
%V ICCIA
%N 1
%P 21-24
%D 2012
%I International Journal of Computer Applications
Abstract

In this age of dramatic technology shift, one of the most significant development has been the emergence of digital video as an important aspect of daily life. While the Internet has significantly changed the way in which we obtain the information, it is much more attractive because of the powerful medium of video. In this paper we have described kernel based object tracking algorithm using mean shift method. The goal of an object tracking algorithm is to generate the trajectory of an object over time by locating its position in every frame of the video. There are various applications of object tracking in the field of computer vision. A smart camera is a very important component for many applications such as, video surveillance, traffic monitoring system and for mobile robots.

References
  1. D. Comaniciu, V. Ramesh, and P. Meer, “Kernel-Based Object Tracking,” IEEE Trans.Pattern Analysis and machine Intelligence, vol. 25, no. 5, May 2003.
  2. Alper Yilmaz, Omar Javed, Mubarak Shah, “Object Tracking: A Survey,” ACM Computing Surveys, vol. 38, no. 4, Article 13, December 2006.
  3. D. Comaniciu, V. Ramesh, and P. Meer, “Real-Time Tracking of Non-Rigid Objects Using Mean Shift,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. II, pp. 142- 149, June 2000
  4. Ismile Haritaoglu, David Harwood, “Real-Time Surveillance of People and Their Activity,” IEEE Trans. On Pattern Analysis and Machine Intelligence, vol. 22, no. 8,August 2000.
  5. D. Comaniciu, and P. Meer, “Mean Shift: A Robust Approach toward Feature Space Analysis,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 5, pp. 603-619, May 2002
  6. llen Bovik, “The Essential Guide to Video Processing,” Academic Press- 2nd Edition 2009.
  7. R. Gonzalez, R. Woods, “Digital Image Processing,” Pearson Prentice Hall Ltd., 2007
Index Terms

Computer Science
Information Sciences

Keywords

Digital image processing Probability Density Function Color Histogram