VIDEO SURVEILLANCE SYSTEM BASED ON GAUSSIAN MIXTURE MODELING WITH TWO-TYPE LEARNING RATE CONTROL SCHEME

In the present invention, we identify that such a tradeoff between robustness to background changes and sensitivity to foreground abnormalities can be easily controlled by a new computational scheme of two-type learning rate control for the Gaussian mixture modeling (GMM). Based on the proposed rate control scheme, a new video surveillance system that applies feedbacks of pixel properties computed in object-level analysis to the learning rate controls of the GMM in pixel-level background modeling is developed. Such a system gives better regularization of background adaptation and is efficient in resolving the tradeoff for many surveillance applications.