Moving objects detection in thermal scene videos using unsupervised Bayesian classifier with bootstrap Gaussian expectation maximization algorithm

In this paper, a new algorithm for moving object detection is proposed by using unsupervised Bayesian classifier with bootstrap Gaussian expectation maximization algorithm. It consists of the following steps: the first contains of classify and estimate the motion vectors between successive frames us...

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Published in:	Multimedia tools and applications Vol. 83; no. 2; pp. 6335 - 6350
Main Author:	Kerfa, Djoudi
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.01.2024 Springer Nature B.V
Subjects:	Algorithms Bayesian analysis Classifiers Computer Communication Networks Computer Science Data Structures and Information Theory Diamonds Maximization Morphology Moving object recognition Multimedia Multimedia Information Systems Optimization Pixels Search algorithms Special Purpose and Application-Based Systems Surveillance Track 6: Computer Vision for Multimedia Applications Gaussian expectation of maximization Unsupervised bayesian classifier Block matching algorithm Infrared video sequences Moving object detection
ISSN:	1380-7501, 1573-7721
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Abstract	In this paper, a new algorithm for moving object detection is proposed by using unsupervised Bayesian classifier with bootstrap Gaussian expectation maximization algorithm. It consists of the following steps: the first contains of classify and estimate the motion vectors between successive frames using the Star diamond search algorithm based on unsupervised Bayesian classifier with Gaussian Expectation of Maximization algorithm, this step serves also to detect the static and dynamic blocks. In the second step, the dynamic blocks are compensated with the white pixels value and the stationary are compensated by black pixels value. In the third step, the morphological opening and closing filters are used for refining the object detected. The proposed approach is trained and evaluated using available infrared (FLIR_ADAS_v2) dataset. The results demonstrate the effectiveness of the proposed method.
AbstractList	In this paper, a new algorithm for moving object detection is proposed by using unsupervised Bayesian classifier with bootstrap Gaussian expectation maximization algorithm. It consists of the following steps: the first contains of classify and estimate the motion vectors between successive frames using the Star diamond search algorithm based on unsupervised Bayesian classifier with Gaussian Expectation of Maximization algorithm, this step serves also to detect the static and dynamic blocks. In the second step, the dynamic blocks are compensated with the white pixels value and the stationary are compensated by black pixels value. In the third step, the morphological opening and closing filters are used for refining the object detected. The proposed approach is trained and evaluated using available infrared (FLIR_ADAS_v2) dataset. The results demonstrate the effectiveness of the proposed method.
Author	Kerfa, Djoudi
Author_xml	– sequence: 1 givenname: Djoudi orcidid: 0000-0002-0615-9464 surname: Kerfa fullname: Kerfa, Djoudi email: dj.kerfa@yahoo.fr organization: National Polytechnic School of Oran Maurice Audin (Ex- Enset)
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Keywords	Gaussian expectation of maximization Unsupervised bayesian classifier Block matching algorithm Infrared video sequences Moving object detection
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SubjectTerms	Algorithms Bayesian analysis Classifiers Computer Communication Networks Computer Science Data Structures and Information Theory Diamonds Maximization Morphology Moving object recognition Multimedia Multimedia Information Systems Optimization Pixels Search algorithms Special Purpose and Application-Based Systems Surveillance Track 6: Computer Vision for Multimedia Applications
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Title	Moving objects detection in thermal scene videos using unsupervised Bayesian classifier with bootstrap Gaussian expectation maximization algorithm
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