Abstract
This paper presents a novel MoG based method for foreground detection and segmentation in video surveillance. Normal MoG is different to deal with the foreground objects that stay in the scene for a long time and segment difficult foreground objects from one blob. We improve MoG by adopting posterior feedback information of Kalman filter tracking, to robustly modeling the background and to perfect the foreground segmentation result. Experiments and comparisons show that our method is robust and accurate in video surveillance. Index Terms—Video surveillance, Mixture of Gaussian, posterior information, Kalman filter tracking.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
