In order to enable traditional monitor systems to detect and track moving objects

a statistical model and GVF- Snake based algorithm is proposed

which employs color video information in static background to detect and track moving objects and provide a good representation of the objects so as to simplify the subsequent object recognition. The algorithm proposed replaces the plain gray space model with normalized RGB space combined with gray space model to eliminate the effect of shadow upon detection

A GMM of the difference of 2 successive frames is constructed

based on which

a motion border image is generated. GVF-Snake is then enhanced to extract the contours of moving objects in video sequence by modifying the energy entry and adding a method to initialize the Snake automatically. In order to accelerate the convergence of the Snake

the contour of next moment is predicted at every moment by estimating the center of the moving object using a 1st-order difference algorithm. This algorithm has been proved to be effective for both rigid and non-rigid objects and can be used for smart surveillance and traffic monitoring