By Topic

Background subtraction using semantic-based hierarchical GMM

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 $31
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

4 Author(s)
Zhao, X. ; Harbin Inst. of Technol., Harbin, China ; Liu, P. ; Liu, J. ; Tang, X.

Background including a long-period fast illumination variation is commonly assumed to be foreground by mistake. To solve this problem, proposed is a semantic-based hierarchical Gaussian mixture model integrated with an illumination detection approach. First, autocorrelation-based features for broad identification of background lighting changes and foreground in short-term sequences are presented. Then, the hierarchical Gaussians representing different background illumination variations are maintained. The effectiveness of the proposed method is demonstrated using experiments on pedestrian detection in fast lighting change.

Published in:

Electronics Letters  (Volume:48 ,  Issue: 14 )