Cart (Loading....) | Create Account
Close category search window
 

Salient Moving Object Detection Using Stochastic Approach Filtering

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.

The purchase and pricing options are temporarily unavailable. Please try again later.
3 Author(s)
Peng Tang ; Sichuan Univ., Chengdu ; Lin Gao ; Zhifang Liu

Background modeling techniques are important for object detection and tracking in video surveillance. Traditional background subtraction approaches are suffered from problems, such as persistent dynamic backgrounds, quick illumination changes, occlusions, noise etc. In this paper, we address the problem of detection and localization of moving objects in a video stream without apperception of background statistics. Three major contributions are presented. First, introducing the Monte Carlo importance sampling techniques greatly reduce the computation complexity while compromise the expected accuracy. Second, the robust salient motion is considered when resampling the feature points by removing those who do not move in a relative constant velocity. Finally, the proposed spatial kinetic mixture of Gaussian model (SKMGM) enforced spatial consistency. Promising results demonstrate the potentials of the proposed framework.

Published in:

Image and Graphics, 2007. ICIG 2007. Fourth International Conference on

Date of Conference:

22-24 Aug. 2007

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.