Publication: Adaptive background modeling from an image sequence by using K-Means clustering
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Issued Date
2010
Resource Type
File Type
application/pdf
Other identifier(s)
2-s2.0-77954896847
Rights Holder(s)
Scopus
Bibliographic Citation
ECTI-CON 2010 - The 2010 ECTI International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology. Vol , No. (2010), p.880-883
Suggested Citation
Charoenpong T., Supasuteekul A., Nuthong C. Adaptive background modeling from an image sequence by using K-Means clustering. ECTI-CON 2010 - The 2010 ECTI International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology. Vol , No. (2010), p.880-883. Retrieved from: https://hdl.handle.net/20.500.14740/7559
Author(s)
Abstract
Background subtraction is an essential technique in vision systems including foreground segmentation, object tracking and video surveillance system. Mixture of Gaussian (MOG) is a popular method for modeling adaptive background in many researches. However, the clustering technique and the number of clusters are different depending on their applications. In this paper, we proposed a novel method for constructing adaptive background from image sequences by using the Gaussian Mixture Model and K-Means clustering technique. Intensities of each pixel in the same coordinate from sequential image are collected. Distribution of intensity is analyzed by the Gaussian Mixture Model. Based on the intensity of background cluster and foreground cluster, the Gaussian distribution is divided into two clusters by K-Means clustering technique. The intensities in the cluster which has maximum member are averaged. The average intensity is used for background model. Nineteen image sequences were done in the experiments. The results show the feasibility of the proposed method.
Subject(s)
Background model
Background modeling
Background subtraction
Clustering techniques
Foreground segmentation
Gaussian Mixture Model
Image sequence
K-means clustering
K-means clustering techniques
Mixture of Gaussians
Novel methods
Number of clusters
Object Tracking
Sequential images
Video surveillance systems
Vision systems
Cluster analysis
Image segmentation
Information technology
Models
Object recognition
Security systems
Water supply systems
Gaussian distribution
Background modeling
Background subtraction
Clustering techniques
Foreground segmentation
Gaussian Mixture Model
Image sequence
K-means clustering
K-means clustering techniques
Mixture of Gaussians
Novel methods
Number of clusters
Object Tracking
Sequential images
Video surveillance systems
Vision systems
Cluster analysis
Image segmentation
Information technology
Models
Object recognition
Security systems
Water supply systems
Gaussian distribution