Adaptive background mixture models for real-time tracking

TLDR

Background subtraction is a common real‑time segmentation technique that thresholds the difference between an estimated background image and the current frame, with many methods varying in model type and update strategy. The paper aims to model each pixel as a mixture of Gaussians and update it online. The adaptive mixture model evaluates Gaussian components to identify those likely from background, classifying each pixel accordingly. The resulting tracker remains stable in real time, handling lighting variations, cluttered repetitive motion, and long‑term scene changes, and has operated continuously for 16 months under rain and snow.

Abstract

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 likely to result from a background process. Each pixel is classified based on whether the Gaussian distribution which represents it most effectively is considered part of the background model. This results in a stable, real-time outdoor tracker which reliably deals with lighting changes, repetitive motions from clutter, and long-term scene changes. This system has been run almost continuously for 16 months, 24 hours a day, through rain and snow.