By Topic

An efficient change detection algorithm based on a statistical nonparametric camera noise model

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)
Bevilacqua, A. ; Dept. of Electron., Comput. Sci. & Syst., Bologna Univ., Italy ; Di Stefano, L. ; Lanza, A.

In this paper we present a change detection algorithm for grey level sequences based on the background subtraction technique, which achieves a good trade-off between time performance and detection quality. The basic idea consists in separating the background process into a deterministic background process and a stochastic camera noise process. The assumption that statistics of the camera noise for a pixel only depends on its current grey level allows to infer a nonparametric statistical camera noise model once and for all arising from a short bootstrap sequence. Hence, 256 couples of lower and upper deterministic thresholds are extracted, to be used in the background subtraction step. While the deterministic nature of the background model as well as of the thresholds lead to an efficient algorithm, utilising 256 couples of different thresholds results in a very sensitive detection. Experimental results allow to assess both the efficiency and the effectiveness of the method we devised.

Published in:

Image Processing, 2004. ICIP '04. 2004 International Conference on  (Volume:4 )

Date of Conference:

24-27 Oct. 2004