By Topic

A novel approach for object extraction from video sequences based on continuous background/foreground classification

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
$33 $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

4 Author(s)

In many computer vision related applications it is necessary to distinguish between the background of an image and the objects that are contained in it. This is a difficult problem because of the constraints imposed by the available time and the computational cost of robust object extraction algorithms. This report describes a new method that benefits from state of the art background/foreground classification combined with the strong theoretical foundations of clustering. The pixels on the scene background are modeled as Mixtures of Gaussians and the output of the classification process are continuous values representing the likelihood that each pixel belongs to the foreground. The clustering is based on a Self Organizing Network (SON) which has a robust initialization schema and is able to find the number of objects in an image or grid. The algorithm's complexity is linear with respect to the number of pixels or cells.

Published in:

Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on

Date of Conference:

18-22 Oct. 2010