Abstract
The rapidness and stability of background extraction from image sequences are incompatible, that is, when a conventional Gaussian mixture models (GMM)is used to rebuild the background, if the background regions of the scene are changed, the extracted background becomes bad until the transition is over. A novel adaptive method is presented to adjust the learning rate of GMM in a Hilbert space. The background extraction is treated as a process of approaching to a certain point in the Hilbert space, so the real-time learning rate can be obtained by calculating the distance between the two adjacent extracted background images, and a judgment method of the stability of background is got too. Compared with conventional GMM, the method has both high rapidness and good stability at the same time, and it can adjust the learning rate online. The experiment shows that it is better than conventional GMM, especially in the transition process of background extraction.
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.
