Illumination-Robust Foreground Detection in a Video Surveillance System

This paper presents a foreground detection algorithm that is robust against illumination changes and noise, and provides a novel and practical choice for intelligent video surveillance systems using static cameras. This paper first introduces an online expectation-maximization algorithm that is deve...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems for video technology 2013-10, Vol.23 (10), p.1637-1650
Main Authors: Dawei Li, Lihong Xu, Goodman, Erik D.
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Background modeling
Cameras
Closed circuit television
Computational modeling
Decision making
Exact sciences and technology
foreground detection
Gaussian mixture model (GMM)
Hidden Markov models
Illumination
Image color analysis
Information, signal and communications theory
Lighting
Linearity
Markov random field (MRF)
Noise
online expectation-maximization (EM)
Pattern recognition
Reflection
Services and terminals of telecommunications
Signal and communications theory
Signal processing
Signal representation. Spectral analysis
Signal, noise
spherical k-means clustering
Studies
Surveillance systems
Systems, networks and services of telecommunications
Telecommunications
Telecommunications and information theory
Telemetry. Remote supervision. Telewarning. Remote control
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Summary:This paper presents a foreground detection algorithm that is robust against illumination changes and noise, and provides a novel and practical choice for intelligent video surveillance systems using static cameras. This paper first introduces an online expectation-maximization algorithm that is developed from a basic batch version to update Gaussian mixture models in real time. Then, a spherical K-means clustering method is combined to provide a more accurate direction for the update when illumination is unstable. The combination is supported by the linearity of RGB color reflected from object surfaces, which is both theoretically proved by spectral reflection theory and experimentally validated in several observations. Foreground detection is carried out using a statistical framework with regional judgment. Noise in the detection stage is further reduced by a Bayesian iterative decision-making step. The experiments show that the proposed algorithm outcompetes several classical methods on several datasets, both in detection performance and in robustness to perturbations from illumination changes.
ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2013.2243649