The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Fire detection using statistical color model in video sequences
Open accessAbstract
In this paper, we propose a real-time fire-detector that combines foreground object information with color pixel statistics of fire. Simple adaptive background model of the scene is generated by using three Gaussian distributions, where each distribution corresponds to the pixel statistics in the respective color channel. The foreground information is extracted by using adaptive background subtraction algorithm, and then verified by the statistical fire color model to determine whether the detected foreground object is a fire candidate or not. A generic fire color model is constructed by statistical analysis of the sample images containing fire pixels. The first contribution of the paper is the application of real-time adaptive background subtraction method that aids the segmentation of the fire candidate pixels from the background. The second contribution is the use of a generic statistical model for refined fire-pixel classification. The two processes are combined to form the fire detection system and applied for the detection of fire in the consecutive frames of video sequences. The frame-processing rate of the detector is about 40 fps with image size of 176x144 pixels, and the algorithm's correct detection rate is 98.89%.
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Davies, A., & Fennessy, P. (2001) · Digital Imaging for Photographers · doi:10.1016/b978-0-240-51590-8.50007-x · Cited by 4,371
Pfinder: real-time tracking of the human body
Wren, C. R., Azarbayejani, A., Darrell, T., & Pentland, A. P. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.598236 · Cited by 4,169
W/sup 4/: real-time surveillance of people and their activities
Haritaoglu, I., Harwood, D., & Davis, L. S. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.868683 · Cited by 2,523
Phillips III, W., Shah, M., & da Vitoria Lobo, N. (2002) · Pattern Recognition Letters · doi:10.1016/s0167-8655(01)00135-0 · Cited by 275
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
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
Airborne Optical and Thermal Remote Sensing for Wildfire Detection and Monitoring
Allison, R., Johnston, J., Craig, G., & Jennings, S. (2016) · Sensors · doi:10.3390/s16081310 · Cited by 304
Metadata
- Authors
- Celik, T., Demirel, H., Ozkaramanli, H., & Uyguroglu, M.
- Venue
- Journal of Visual Communication and Image Representation
- Publisher
- Elsevier BV
- Published
- 2007-04
- Volume
- 18
- Issue
- 2
- Pages
- 176-185
- ISSN
- 1047-3203 (ISSN-L)
- OpenAlex
- W2103941746
- Cited by
- 342
- References
- 4
- FWCI
- 33.15
- Type
- journal article
- Language
- en
- Open access
- green
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- Celik, T., Demirel, H., Ozkaramanli, H., & Uyguroglu, M. (2007). Fire detection using statistical color model in video sequences. Journal of Visual Communication and Image Representation. https://doi.org/10.1016/j.jvcir.2006.12.003
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