The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Fire detection in video sequences using a generic color model
Open accessAbstract
In this paper, a rule-based generic color model for flame pixel classification is proposed. The proposed algorithm uses YCbCr color space to separate the luminance from the chrominance more effectively than color spaces such as RGB or rgb. The performance of the proposed algorithm is tested on two sets of images, one of which contains fire, the other containing fire-like regions. The proposed method achieves up to 99% fire detection rate. The results are compared with two other methods in the literature and the proposed method is shown to have both a higher detection rate and a lower false alarm rate. Furthermore the proposed color model can be used for real-time fire detection in color video sequences, and we also present results for segmentation of fire in video using only the color model proposed in this paper.
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Cites
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An early fire-detection method based on image processing
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Computer vision based method for real-time fire and flame detection
Töreyin, B. U., Dedeoğlu, Y., Güdükbay, U., & Çetin, A. E. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.06.015 · Cited by 639
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An image processing technique for fire detection in video images
Marbach, G., Loepfe, M., & Brupbacher, T. (2006) · Fire Safety Journal · doi:10.1016/j.firesaf.2006.02.001 · Cited by 275
Flame detection in video using hidden Markov models
Töreyın, B. U., Dedeoğlu, Y., & Çetin, A. E. (2005) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2005.1530284 · Cited by 209
Cited by
Yuan, C., Zhang, Y., & Liu, Z. (2015) · Canadian Journal of Forest Research · doi:10.1139/cjfr-2014-0347 · Cited by 639
Muhammad, K., Ahmad, J., & Baik, S. W. (2018) · Neurocomputing · doi:10.1016/j.neucom.2017.04.083 · Cited by 558
Convolutional Neural Networks Based Fire Detection in Surveillance Videos
Muhammad, K., Ahmad, J., Mehmood, I., Rho, S., & Baik, S. W. (2018) · IEEE Access · doi:10.1109/access.2018.2812835 · Cited by 556
Efficient Deep CNN-Based Fire Detection and Localization in Video Surveillance Applications
Muhammad, K., Ahmad, J., Lv, Z., Bellavista, P., Yang, P., & Baik, S. W. (2019) · IEEE Transactions on Systems, Man, and Cybernetics: Systems · doi:10.1109/tsmc.2018.2830099 · Cited by 537
Foggia, P., Saggese, A., & Vento, M. (2015) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2015.2392531 · Cited by 483
Metadata
- Authors
- Çelik, T., & Demirel, H.
- Venue
- Fire Safety Journal
- Publisher
- Elsevier BV
- Published
- 2009-02
- Volume
- 44
- Issue
- 2
- Pages
- 147-158
- ISSN
- 0379-7112 (ISSN-L)
- OpenAlex
- W2011160765
- Cited by
- 553
- References
- 8
- FWCI
- 45.11
- Type
- journal article
- Language
- en
- Open access
- green
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
