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Big data oriented novel background subtraction algorithm for urban surveillance systems

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Big data oriented novel background subtraction algorithm for urban surveillance systems. / Hu, Ling; Ni, Qiang; Yuan, Feng.
In: Big Data Mining and Analytics, Vol. 1, No. 2, 06.2018, p. 137-145.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Hu, L, Ni, Q & Yuan, F 2018, 'Big data oriented novel background subtraction algorithm for urban surveillance systems', Big Data Mining and Analytics, vol. 1, no. 2, pp. 137-145. https://doi.org/10.26599/BDMA.2018.9020013

APA

Hu, L., Ni, Q., & Yuan, F. (2018). Big data oriented novel background subtraction algorithm for urban surveillance systems. Big Data Mining and Analytics, 1(2), 137-145. https://doi.org/10.26599/BDMA.2018.9020013

Vancouver

Hu L, Ni Q, Yuan F. Big data oriented novel background subtraction algorithm for urban surveillance systems. Big Data Mining and Analytics. 2018 Jun;1(2):137-145. Epub 2018 Apr 12. doi: 10.26599/BDMA.2018.9020013

Author

Hu, Ling ; Ni, Qiang ; Yuan, Feng. / Big data oriented novel background subtraction algorithm for urban surveillance systems. In: Big Data Mining and Analytics. 2018 ; Vol. 1, No. 2. pp. 137-145.

Bibtex

@article{db40ebfffac04feca78385d551fb1929,
title = "Big data oriented novel background subtraction algorithm for urban surveillance systems",
abstract = "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.",
author = "Ling Hu and Qiang Ni and Feng Yuan",
note = "{\textcopyright}2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.",
year = "2018",
month = jun,
doi = "10.26599/BDMA.2018.9020013",
language = "English",
volume = "1",
pages = "137--145",
journal = "Big Data Mining and Analytics",
issn = "2096-0654",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "2",

}

RIS

TY - JOUR

T1 - Big data oriented novel background subtraction algorithm for urban surveillance systems

AU - Hu, Ling

AU - Ni, Qiang

AU - Yuan, Feng

N1 - ©2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2018/6

Y1 - 2018/6

N2 - 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.

AB - 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.

U2 - 10.26599/BDMA.2018.9020013

DO - 10.26599/BDMA.2018.9020013

M3 - Journal article

VL - 1

SP - 137

EP - 145

JO - Big Data Mining and Analytics

JF - Big Data Mining and Analytics

SN - 2096-0654

IS - 2

ER -