Random Block Background Modelling for Foreground Detection in UHD videos

• Item type: SIG Technical Reports(1)
• 公開日: 2016-01-14
• タイトル: Random Block Background Modelling for Foreground Detection in UHD videos
• タイトル / 言語: en
• タイトル: Random Block Background Modelling for Foreground Detection in UHD videos
• 言語: eng
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: Graduate School of Information, Production and Systems, Waseda University
• 著者所属: Graduate School of Information, Production and Systems, Waseda University
• 著者所属: Graduate School of Information, Production and Systems, Waseda University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information, Production and Systems, Waseda University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information, Production and Systems, Waseda University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information, Production and Systems, Waseda University
• 著者名: Axel, Beaugendre; Satoshi, Goto; Takeshi, Yoshimura
• 著者名(英): Axel, Beaugendre; Satoshi, Goto; Takeshi, Yoshimura
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Conventional foreground detection methods can take hours to detect objects in a single 4K Ultra High Definition (UHD) frame and their memory requirement is too high to be used without a huge investment in dedicated hardware systems. The proposed Random Block Background Modelling (RBBM) is a spatio-temporal method designed to update quickly the background image of UHD videos. By dividing the image into Mega-Blocks, themselves containing smaller Sub-Blocks and by using small randomly selected Sub-Blocks at each frame through a Gaussian average, the RBBM can accelerate the background modelling. Then, the RBBM is used in combination with a Block Propagative Background Subtraction method to detect the foreground. The proposed RBBM method has been compared with multiple other state-of-the-art works on 4 categories of UHD 4K scenes. The RBBM shows the best quality performances, the best ratio processing time per pixel/quality and a low memory requirement.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Conventional foreground detection methods can take hours to detect objects in a single 4K Ultra High Definition (UHD) frame and their memory requirement is too high to be used without a huge investment in dedicated hardware systems. The proposed Random Block Background Modelling (RBBM) is a spatio-temporal method designed to update quickly the background image of UHD videos. By dividing the image into Mega-Blocks, themselves containing smaller Sub-Blocks and by using small randomly selected Sub-Blocks at each frame through a Gaussian average, the RBBM can accelerate the background modelling. Then, the RBBM is used in combination with a Block Propagative Background Subtraction method to detect the foreground. The proposed RBBM method has been compared with multiple other state-of-the-art works on 4 categories of UHD 4K scenes. The RBBM shows the best quality performances, the best ratio processing time per pixel/quality and a low memory requirement.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA11131797
• 書誌情報: 研究報告コンピュータビジョンとイメージメディア（CVIM）; 巻 2016-CVIM-200, 号 27, p. 1-6, 発行日 2016-01-14
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2188-8701
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会