A Content-Adaptive Resizing Framework for Boosting Computation Speed of Background Modeling Methods.
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| Title: | A Content-Adaptive Resizing Framework for Boosting Computation Speed of Background Modeling Methods. |
|---|---|
| Authors: | Huang, Chun-Rong1 (AUTHOR) crhuang@nchu.edu.tw, Huang, Wei-Yun1 (AUTHOR) rm3028@alumni.nchu.edu.tw, Liao, Yi-Sheng1 (AUTHOR) g105056037@mail.nchu.edu.tw, Lee, Chien-Cheng2 (AUTHOR) cclee@saturn.yzu.edu.tw, Yeh, Yu-Wei3 (AUTHOR) s1044828@mail.yzu.edu.tw |
| Source: | IEEE Transactions on Systems, Man & Cybernetics. Systems. Jan2022, Vol. 52 Issue 1, p1192-1204. 13p. |
| Subjects: | Video surveillance, Source code, Graphics processing units, Speed |
| Abstract: | Recently, most background modeling (BM) methods claim to achieve real-time efficiency for low-resolution and standard-definition surveillance videos. With the increasing resolutions of surveillance cameras, full high-definition (full HD) surveillance videos have become the main trend and thus processing high-resolution videos becomes a novel issue in intelligent video surveillance. In this article, we propose a novel content-adaptive resizing framework (CARF) to boost the computation speed of BM methods in high-resolution surveillance videos. For each frame, we apply superpixels to separate the content of the frame to homogeneous and boundary sets. Two novel downsampling and upsampling layers based on the homogeneous and boundary sets are proposed. The front one downsamples high-resolution frames to low-resolution frames for obtaining efficient foreground segmentation results based on BM methods. The later one upsamples the low-resolution foreground segmentation results to the original resolution frames based on the superpixels. By simultaneously coupling both layers, experimental results show that the proposed method can achieve better quantitative and qualitative results compared with state-of-the-art methods. Moreover, the computation speed of the proposed method without GPU accelerations is also significantly faster than that of the state-of-the-art methods. The source code is available at https://github.com/nchucvml/CARF. [ABSTRACT FROM AUTHOR] |
| Copyright of IEEE Transactions on Systems, Man & Cybernetics. Systems is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 154801012 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Content-Adaptive Resizing Framework for Boosting Computation Speed of Background Modeling Methods. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Huang%2C+Chun-Rong%22">Huang, Chun-Rong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> crhuang@nchu.edu.tw</i><br /><searchLink fieldCode="AR" term="%22Huang%2C+Wei-Yun%22">Huang, Wei-Yun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> rm3028@alumni.nchu.edu.tw</i><br /><searchLink fieldCode="AR" term="%22Liao%2C+Yi-Sheng%22">Liao, Yi-Sheng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> g105056037@mail.nchu.edu.tw</i><br /><searchLink fieldCode="AR" term="%22Lee%2C+Chien-Cheng%22">Lee, Chien-Cheng</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> cclee@saturn.yzu.edu.tw</i><br /><searchLink fieldCode="AR" term="%22Yeh%2C+Yu-Wei%22">Yeh, Yu-Wei</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> s1044828@mail.yzu.edu.tw</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Systems%2C+Man+%26+Cybernetics%2E+Systems%22">IEEE Transactions on Systems, Man & Cybernetics. Systems</searchLink>. Jan2022, Vol. 52 Issue 1, p1192-1204. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Video+surveillance%22">Video surveillance</searchLink><br /><searchLink fieldCode="DE" term="%22Source+code%22">Source code</searchLink><br /><searchLink fieldCode="DE" term="%22Graphics+processing+units%22">Graphics processing units</searchLink><br /><searchLink fieldCode="DE" term="%22Speed%22">Speed</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Recently, most background modeling (BM) methods claim to achieve real-time efficiency for low-resolution and standard-definition surveillance videos. With the increasing resolutions of surveillance cameras, full high-definition (full HD) surveillance videos have become the main trend and thus processing high-resolution videos becomes a novel issue in intelligent video surveillance. In this article, we propose a novel content-adaptive resizing framework (CARF) to boost the computation speed of BM methods in high-resolution surveillance videos. For each frame, we apply superpixels to separate the content of the frame to homogeneous and boundary sets. Two novel downsampling and upsampling layers based on the homogeneous and boundary sets are proposed. The front one downsamples high-resolution frames to low-resolution frames for obtaining efficient foreground segmentation results based on BM methods. The later one upsamples the low-resolution foreground segmentation results to the original resolution frames based on the superpixels. By simultaneously coupling both layers, experimental results show that the proposed method can achieve better quantitative and qualitative results compared with state-of-the-art methods. Moreover, the computation speed of the proposed method without GPU accelerations is also significantly faster than that of the state-of-the-art methods. The source code is available at https://github.com/nchucvml/CARF. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IEEE Transactions on Systems, Man & Cybernetics. Systems is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1109/TSMC.2020.3018872 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 1192 Subjects: – SubjectFull: Video surveillance Type: general – SubjectFull: Source code Type: general – SubjectFull: Graphics processing units Type: general – SubjectFull: Speed Type: general Titles: – TitleFull: A Content-Adaptive Resizing Framework for Boosting Computation Speed of Background Modeling Methods. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Huang, Chun-Rong – PersonEntity: Name: NameFull: Huang, Wei-Yun – PersonEntity: Name: NameFull: Liao, Yi-Sheng – PersonEntity: Name: NameFull: Lee, Chien-Cheng – PersonEntity: Name: NameFull: Yeh, Yu-Wei IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Jan2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 21682216 Numbering: – Type: volume Value: 52 – Type: issue Value: 1 Titles: – TitleFull: IEEE Transactions on Systems, Man & Cybernetics. Systems Type: main |
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