Fire Detection

This fire detection project is organized to unite algorithms since there are so many existing rule-based algorithms that I wrote this keeping my mind on extension. So this project can add other algorithms or be modified. Most sample videos I tested are from [1] and [2].
1. Candidate Extraction
The fire candidates can be extracted at the very first because it is better to search selectively fire region than to the whole frame in terms of performance. Setting fire-like color range of RGB or color weight through machine learning is general, so I applied color weight algorithm from [3] as pre-processing. In addition, color-balancing such as [4] can be also added for nighttime environment.
2. Red Channel Based Detection

3. Covariance Based Detection


4. Flow Rate Based Detection

Reference
[2] https://zenodo.org/record/836749#.XK85hugzYlU
[3] J. Choi and J. Y. Choi, Patch-based fire detection with online outlier learning, 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Karlsruhe, 2015, pp. 1-6.
[4] http://pi-virtualworld.blogspot.com/2013/09/color-constancy-gray-world-algorithm.html
[5] Hakan Habiboğlu, Yusuf & Günay, Osman & Cetin, A. (2011). Covariance matrix-based fire and flame detection method in video. Machine Vision and Applications. 23. 1-11. 10.1007/s00138-011-0369-1.
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