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Adaptively Splitted GMM with Feedback Improvement for the Task of Background Subtraction
Citation key 1441Evangelio2014
Author Rubén Heras Evangelio and Michael Pätzold and Ivo Keller and Thomas Sikora
Pages 863–874
Year 2014
DOI 10.1109/TIFS.2014.2313919
Journal IEEE Transactions on Information Forensics & Security
Volume 9
Number 5
Month may
Abstract Per pixel adaptive Gaussian Mixture Models (GMM) have become a popular choice for the detection of change in the video surveillance domain because of their ability to cope with many challenges characteristic for surveillance systems in real-time with low memory requirements. Since their first introduction in the surveillance domain, GMM have been enhanced in many directions. In this paper, we present a study of some relevant GMM approaches and analyze their underlying assumptions and design decisions. Based on this study, we show how these systems can be further improved by means of a variance controlling scheme and the incorporation of region analysis based feedback. The proposed system has been thoroughly evaluated using the extensive dataset of the IEEE Workshop on Change Detection, showing an outranking performance in comparison with state-of-the-art methods.
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