1997 ImageSegmentationinVideoSequenc

(Friedman & Russell, 1997) ⇒ Nir Friedman, and Stuart Russell. (1997). “Image Segmentation in Video Sequences: A Probabilistic Approach.” In: Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence.

Subject Headings: Expectation Maximization; Finite Mixture Models

Notes

Cited By

Quotes

Abstract

“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.

References

,

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
1997 ImageSegmentationinVideoSequenc	Stuart J. Russell Nir Friedman			Image Segmentation in Video Sequences: A Probabilistic Approach