@article{ref1,
title="Robust background subtraction with foreground validation for urban traffic video",
journal="EURASIP journal on advances in signal processing",
year="2005",
author="Cheung, S. C.S and Kamath, C.",
volume="2005",
number="14",
pages="2330-2340",
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.<p />",
language="",
issn="1687-6172",
doi="10.1155/ASP.2005.2330",
url="http://dx.doi.org/10.1155/ASP.2005.2330"
}