A Novel Efficient Video Smoke Detection Algorithm Using Co-occurrence of Local Binary Pattern Variants-Reference-Cited by-同舟云学术

A Novel Efficient Video Smoke Detection Algorithm Using Co-occurrence of Local Binary Pattern Variants

Published:2022-08-01 Issue:5 Volume:58 Page:3139-3165
ISSN:0015-2684
Container-title:Fire Technology
language:en
Short-container-title:Fire Technol

Author:

Prema C. Emmy^ORCID,Suresh S.,Krishnan M. Navaneetha,Leema N.

Publisher

Springer Science and Business Media LLC

Subject

Safety, Risk, Reliability and Quality,General Materials Science,Building and Construction

Link

https://link.springer.com/content/pdf/10.1007/s10694-022-01306-2.pdf

Reference33 articles.

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2. Favorskaya M, Pyataeva A, Popov A (2015) Verification of smoke detection in video sequences based on spatiotemporal local binary patterns. Procedia Comput Sci 60:671–680

3. Lin G, Zhang Y, Zhang Q, Jia Y, Gao Xu, Wang J (2017) Smoke detection in video sequences based on dynamic texture using volume local binary patterns. KSII T Internet Info 11(11):5522–5536

4. Chongyuan Tao, Jian Zhang, Pan Wang (2010) Smoke Detection Based on Deep Convolutional Neural Networks. 2016 International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration. https://doi.org/10.1109/ICIICII.2016.0045

5. Qureshi WS, Ekpanyapong M, Dailey MN, Rinsurongkawong S, Malenichev A, Krasotkina O (2016) QuickBlaze: Early Fire Detection Using a Combined Video Processing Approach. Fire Technol 52:1293–1317

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