Spatiotemporal Gaussian mixture model to detect moving objects in dynamic scenes

Wei Zhang; Xiangzhong Fang; Xiaokang Yang; Q. M. Jonathan Wu

doi:10.1117/1.2731329

1 April 2007 Spatiotemporal Gaussian mixture model to detect moving objects in dynamic scenes

Wei Zhang, Xiangzhong Fang, Xiaokang Yang, Q. M. Jonathan Wu

Author Affiliations +

Journal of Electronic Imaging, Vol. 16, Issue 2, 023013 (April 2007). https://doi.org/10.1117/1.2731329

Abstract

The Gaussian mixture model (GMM) is an important metric for moving objects segmentation and is fit to deal with the gradual changes of illumination and the repetitive motions of scene elements. However, the performance of the GMM may be plagued by the complex motion of the dynamic background such as waving trees and flags fluttering. A spatiotemporal Gaussian mixture model (STGMM) is proposed to handle the complex motion of the background by considering every background pixel to be fluctuating both in intensity and in its neighboring region. A new matching rule is defined to incorporate the spatial information. Experimental results on typical scenes show that STGMM can segment the moving objects correctly in complex scenes. Quantitative evaluations demonstrate that the proposed STGMM performs better than GMM.

©(2007) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Wei Zhang, Xiangzhong Fang, Xiaokang Yang, and Q. M. Jonathan Wu "Spatiotemporal Gaussian mixture model to detect moving objects in dynamic scenes," Journal of Electronic Imaging 16(2), 023013 (1 April 2007). https://doi.org/10.1117/1.2731329

Published: 1 April 2007

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available