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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| Vydané v: | Multimedia tools and applications Ročník 79; číslo 9-10; s. 5919 - 5940 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
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New York
Springer US
01.03.2020
Springer Nature B.V |
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| 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. |
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| 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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| Keywords | Wavelet Statistical parameters Background subtraction Moving object detection Morphology |
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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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