Big Data Oriented Novel Background Subtraction Algorithm for Urban Surveillance Systems
Due to the tremendous volume of data generated by urban surveillance systems, big data oriented low-complexity automatic background subtraction techniques are in great demand. In this paper, we propose a novel automatic background subtraction algorithm for urban surveillance systems in which the com...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2018-06-01
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| Series: | Big Data Mining and Analytics |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2018.9020013 |
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| _version_ | 1832568969359261696 |
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| author | Ling Hu Qiang Ni Feng Yuan |
| author_facet | Ling Hu Qiang Ni Feng Yuan |
| author_sort | Ling Hu |
| collection | DOAJ |
| description | Due to the tremendous volume of data generated by urban surveillance systems, big data oriented low-complexity automatic background subtraction techniques are in great demand. In this paper, we propose a novel automatic background subtraction algorithm for urban surveillance systems in which the computer can automatically renew an image as the new background image when no object is detected. This method is both simple and robust with respect to changes in light conditions. |
| format | Article |
| id | doaj-art-34d4aedc817c447d9ef754ab1e74263a |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2018-06-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-34d4aedc817c447d9ef754ab1e74263a2025-02-02T23:47:25ZengTsinghua University PressBig Data Mining and Analytics2096-06542018-06-011213714510.26599/BDMA.2018.9020013Big Data Oriented Novel Background Subtraction Algorithm for Urban Surveillance SystemsLing Hu0Qiang Ni1Feng Yuan2<institution>School of Computing and Communications, Lancaster University, InfoLab21</institution>, <city>Lancaster</city>, <postal-code>LA1 4WA</postal-code>.<institution>School of Computing and Communications, Lancaster University, InfoLab21</institution>, <city>Lancaster</city>, <postal-code>LA1 4WA</postal-code>.<institution>Chinese Academy of Sciences Smart City Software Co. Ltd.</institution>, <country>China</country> and also with <institution content-type="dept">Institute of Software Application Technology</institution>, <institution>Guangzhou & Chinese Academy of Sciences</institution>, <city>Guangzhou </city><postal-code>511458</postal-code>, <country>China</country>.Due to the tremendous volume of data generated by urban surveillance systems, big data oriented low-complexity automatic background subtraction techniques are in great demand. In this paper, we propose a novel automatic background subtraction algorithm for urban surveillance systems in which the computer can automatically renew an image as the new background image when no object is detected. This method is both simple and robust with respect to changes in light conditions.https://www.sciopen.com/article/10.26599/BDMA.2018.9020013big databackground subtractionurban surveillance systems |
| spellingShingle | Ling Hu Qiang Ni Feng Yuan Big Data Oriented Novel Background Subtraction Algorithm for Urban Surveillance Systems Big Data Mining and Analytics big data background subtraction urban surveillance systems |
| title | Big Data Oriented Novel Background Subtraction Algorithm for Urban Surveillance Systems |
| title_full | Big Data Oriented Novel Background Subtraction Algorithm for Urban Surveillance Systems |
| title_fullStr | Big Data Oriented Novel Background Subtraction Algorithm for Urban Surveillance Systems |
| title_full_unstemmed | Big Data Oriented Novel Background Subtraction Algorithm for Urban Surveillance Systems |
| title_short | Big Data Oriented Novel Background Subtraction Algorithm for Urban Surveillance Systems |
| title_sort | big data oriented novel background subtraction algorithm for urban surveillance systems |
| topic | big data background subtraction urban surveillance systems |
| url | https://www.sciopen.com/article/10.26599/BDMA.2018.9020013 |
| work_keys_str_mv | AT linghu bigdataorientednovelbackgroundsubtractionalgorithmforurbansurveillancesystems AT qiangni bigdataorientednovelbackgroundsubtractionalgorithmforurbansurveillancesystems AT fengyuan bigdataorientednovelbackgroundsubtractionalgorithmforurbansurveillancesystems |