A common method for real-time segmentation of

moving regions in image sequences involves “background

subtraction,” or thresholding the error between

an estimate of the image without moving objects and

the current image. The numerous approaches to this

problem differ in the type of background model used

and the procedure used to update the model. This paper

discusses modeling each pixel as a mixture of Gaussians

and using an on-line approximation to update

the model. The Gaussian distributions of the adaptive

mixture model are then evaluated to determine which

are most likelyt o result from a background process.

Each pixel is classified based on whether the Gaussian

distribution which represents it most effectivelyis considered

part of the background model.