DMCA
Nonparametric model for background subtraction (2000)
| Venue: | in ECCV ’00 |
| Citations: | 536 - 17 self |
BibTeX
@INPROCEEDINGS{Elgammal00nonparametricmodel,
author = {Ahmed Elgammal and David Harwood and Larry Davis},
title = {Nonparametric model for background subtraction},
booktitle = {in ECCV ’00},
year = {2000},
pages = {751--767}
}
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Abstract
Abstract. Background subtraction is a method typically used to seg-ment moving regions in image sequences taken from a static camera by comparing each new frame to a model of the scene background. We present a novel non-parametric background model and a background subtraction approach. The model can handle situations where the back-ground of the scene is cluttered and not completely static but contains small motions such as tree branches and bushes. The model estimates the probability of observing pixel intensity values based on a sample of intensity values for each pixel. The model adapts quickly to changes in the scene which enables very sensitive detection of moving targets. We also show how the model can use color information to suppress detec-tion of shadows. The implementation of the model runs in real-time for both gray level and color imagery. Evaluation shows that this approach achieves very sensitive detection with very low false alarm rates. Key words: visual motion, active and real time vision, motion detection, non-parametric estimation, visual surveillance, shadow detection 1
Keyphrases
background subtraction nonparametric model sensitive detection non-parametric estimation motion detection color imagery intensity value new frame color information visual motion background subtraction approach shadow detection visual surveillance static camera image sequence scene background real time vision gray level small motion tree branch novel non-parametric background model key word low false alarm rate pixel intensity value
