"Background subtraction" is an old technique for finding moving objects in a video sequence-for example, cars driving on a freeway. The idea is that subtracting the current image from a time averaged background image will leave only non stationary objects. It is, however, a crude ap proximation to the task of classifying each pixel of the current image; it fails with slow-moving objects and does not distinguish shadows from moving objects. The basic idea of this paper is that we can classify each pixel using a model of how that pixel looks when it is part of different classes. We learn a mixture-of-Gaussians classi fication model for each pixel using an unsuper vised technique-an efficient, incremental ver sion of EM. Unlike the standard image-averaging approach, this automatically updates the mixture component for each class according to likelihood of membership; hence slow-moving objects are handled perfectly. Our approach also identifies and eliminates shadows much more effectively than other techniques such as thresholding. Ap plication of this method as part of the Roadwatch traffic surveillance project is expected to result in significant improvements in vehicle identification and tracking.
In REF , the mixture of Gaussians approach was used in a traffic monitoring application.
Image Segmentation in Video Sequences: A Probabilistic Approach
ArXiv
ArXiv
Computer Science