Image Segmentation in Video Sequences: A Probabilistic Approach
N. Friedman and S. Russell
To appear in Proc. Thirteenth Conf. on
Uncertainty in Artificial Intelligence (UAI 97).
``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 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.
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