Open Access

Robust Background Subtraction with Foreground Validation for Urban Traffic Video

EURASIP Journal on Advances in Signal Processing20052005:726261

https://doi.org/10.1155/ASP.2005.2330

Received: 15 January 2004

Published: 25 August 2005

Abstract

Identifying moving objects in a video sequence is a fundamental and critical task in many computer-vision applications. Background subtraction techniques are commonly used to separate foreground moving objects from the background. Most background subtraction techniques assume a single rate of adaptation, which is inadequate for complex scenes such as a traffic intersection where objects are moving at different and varying speeds. In this paper, we propose a foreground validation algorithm that first builds a foreground mask using a slow-adapting Kalman filter, and then validates individual foreground pixels by a simple moving object model built using both the foreground and background statistics as well as the frame difference. Ground-truth experiments with urban traffic sequences show that our proposed algorithm significantly improves upon results using only Kalman filter or frame-differencing, and outperforms other techniques based on mixture of Gaussians, median filter, and approximated median filter.

Keywords and phrases

background subtraction foreground validation urban traffic video

Authors’ Affiliations

(1)
Center for Applied Scientific Computing, Lawrence Livermore National Laboratory
(2)
Department of Electrical and Computer Engineering, University of Kentucky

Copyright

© Cheung and Kamath 2005