Date of Award
2009
Publication Type
Master Thesis
Degree Name
M.Sc.
Department
Computer Science
Keywords
Applied sciences
Supervisor
Dr. Imran Ahmad
Rights
info:eu-repo/semantics/openAccess
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Abstract
Intelligent visual surveillance which attempts to detect, recognize and track certain objects from image sequences is becoming an active research topic in computer vision community. Background modeling and foreground segmentation are the first two and the most important steps in any intelligent visual surveillance systems. The accuracy of these two steps highly effects performance of the following steps. In this thesis, we propose a simple and novel method which employs histogram based median method for background modeling and a fuzzy k-Means clustering approach for foreground segmentation. Experiments on a set of videos and benchmark image sequences show the effectiveness of the proposed method. Compared with other two contemporary methods - k -Means clustering and Mixture of Gaussians (MoG) - the proposed method is not only time efficient but also provides better segmentation results.
Recommended Citation
Yao, Huajing, "Adaptive foreground segmentation using fuzzy approach" (2009). Electronic Theses and Dissertations. 7989.
https://scholar.uwindsor.ca/etd/7989