By Topic

Multiple object tracking using improved GMM-based motion segmentation

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Fazli, S. ; Electr. Eng. Dept., Zanjan Univ., Zanjan ; Pour, H.M. ; Bouzari, H.

Human tracking in dynamic scenes has been an important topic of research. This paper presents a novel and robust algorithm for multiple motion detection and tracking in dynamic and complex scenes. The algorithm consists of two steps: at first, we use a robust algorithm for human detection. Then, Gaussian mixture model (GMM), Neighborhood-based difference and Overlapping-based classification are applied to improve human detection performance. The conventional mixture Gaussian method suffers from false motion detection in complex backgrounds and slow convergence. We combine three above mentioned methods to obtain robust motion detection. The second step of the proposed algorithm is object tracking framework based on Kalman filtering which works well in dynamic scenes. Experimental results show the high performance of the proposed method for multiple object tracking in complex and noisy backgrounds.

Published in:

Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 2009. ECTI-CON 2009. 6th International Conference on  (Volume:02 )

Date of Conference:

6-9 May 2009