Moving object detection using statistical background subtraction in wavelet compressed domain

Moving object detection is a fundamental task and extensively used research area in modern world computer vision applications. Background subtraction is one of the widely used and the most efficient technique for it, which generates the initial background using different statistical parameters. Due...

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Published in:	Multimedia tools and applications Vol. 79; no. 9-10; pp. 5919 - 5940
Main Authors:	Sengar, Sandeep Singh, Mukhopadhyay, Susanta
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.03.2020 Springer Nature B.V
Subjects:	Algorithms Aperture Computer Communication Networks Computer Science Computer vision Data Structures and Information Theory Domains Morphology Moving object recognition Multimedia Multimedia Information Systems Optimization techniques Parameters Pixels Principal components analysis Segmentation Special Purpose and Application-Based Systems Subtraction Surveillance Video data Wavelet transforms Wavelet Statistical parameters Background subtraction Moving object detection Morphology
ISSN:	1380-7501, 1573-7721
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Abstract	Moving object detection is a fundamental task and extensively used research area in modern world computer vision applications. Background subtraction is one of the widely used and the most efficient technique for it, which generates the initial background using different statistical parameters. Due to the enormous size of the video data, the segmentation process requires considerable amount of memory space and time. To reduce the above shortcomings, we propose a statistical background subtraction based motion segmentation method in a compressed transformed domain employing wavelet. We employ the weighted-mean and weighted-variance based background subtraction operations only on the detailed components of the wavelet transformed frame to reduce the computational complexity. Here, weight for each pixel location is computed using pixel-wise median operation between the successive frames. To detect the foreground objects, we employ adaptive threshold, the value of which is selected based on different statistical parameters. Finally, morphological operation, connected component analysis, and flood-fill algorithm are applied to efficiently and accurately detect the foreground objects. Our method is conceived, implemented, and tested on different real video sequences and experimental results show that the performance of our method is reasonably better compared to few other existing approaches.
AbstractList	Moving object detection is a fundamental task and extensively used research area in modern world computer vision applications. Background subtraction is one of the widely used and the most efficient technique for it, which generates the initial background using different statistical parameters. Due to the enormous size of the video data, the segmentation process requires considerable amount of memory space and time. To reduce the above shortcomings, we propose a statistical background subtraction based motion segmentation method in a compressed transformed domain employing wavelet. We employ the weighted-mean and weighted-variance based background subtraction operations only on the detailed components of the wavelet transformed frame to reduce the computational complexity. Here, weight for each pixel location is computed using pixel-wise median operation between the successive frames. To detect the foreground objects, we employ adaptive threshold, the value of which is selected based on different statistical parameters. Finally, morphological operation, connected component analysis, and flood-fill algorithm are applied to efficiently and accurately detect the foreground objects. Our method is conceived, implemented, and tested on different real video sequences and experimental results show that the performance of our method is reasonably better compared to few other existing approaches.
Author	Mukhopadhyay, Susanta Sengar, Sandeep Singh
Author_xml	– sequence: 1 givenname: Sandeep Singh orcidid: 0000-0003-2171-9332 surname: Sengar fullname: Sengar, Sandeep Singh email: sandeep.iitdhanbad@gmail.com organization: Department of Computer Science & Engineering, SRM University-AP – sequence: 2 givenname: Susanta surname: Mukhopadhyay fullname: Mukhopadhyay, Susanta organization: Department of Computer Science & Engineering, Indian Institute of Technology (ISM)
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SubjectTerms	Algorithms Aperture Computer Communication Networks Computer Science Computer vision Data Structures and Information Theory Domains Morphology Moving object recognition Multimedia Multimedia Information Systems Optimization techniques Parameters Pixels Principal components analysis Segmentation Special Purpose and Application-Based Systems Subtraction Surveillance Video data Wavelet transforms
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Title	Moving object detection using statistical background subtraction in wavelet compressed domain
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