Improved adaptive Gaussian mixture model for background subtraction

Background subtraction is a common computer vision task. We analyze the usual pixel-level approach. We develop an efficient adaptive algorithm using Gaussian mixture probability density. Recursive equations are used to constantly update the parameters and but also to simultaneously select the appropriate number of components for each pixel.

1. Introduction

A static camera observing a scene is a common case of a surveillance system. Detecting intruding objects is an essential step in analyzing the scene. An usually applicable assumption is that the images of the scene without the intruding objects exhibit some regular behavior that can be well described by a statistical model. If we have a statistical model of the scene, an intruding object can be detected by spotting the parts of the image that don't fit the model. This process is usually known as “background subtraction”.