  "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 timeaveraged background image will leave
only nonstationary objects. It is, however, a crude approximation 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
classification model for each pixel using an unsupervised technique- an
efficient, incremental version 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. Application of this
method as part of the Roadwatch traffic surveillance project is expected to
result in significant improvements in vehicle identification and tracking.
