Vision based fire detection
By Che-Bin Liu, Narendra Ahuja. Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., 2004
295 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Safety, Risk, Reliability and Quality
View Paper: https://www.semanticscholar.org/paper/9ab3cb1a34562f550f309091d639400a988e1992
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Kosmas Dimitropoulos
- Khan Muhammad
- A. Cetin
- Sung Wook Baik
- B. U. Töreyin
- Jamil Ahmad
- Yigithan Dedeoglu
In Electrical and Electronic Engineering
In Safety, Risk, Reliability and Quality
- Kosmas Dimitropoulos
- Khan Muhammad
- A. Cetin
- Sung Wook Baik
- B. U. Töreyin
- Jamil Ahmad
- Yigithan Dedeoglu
Citations in the corpus
- Wavelet based real-time smoke detection in video (2005, background): “Methods of identifyingfire in video include [1], [2], [3], [ 10 ] and [11].”
- Video fire detection - Review (2013, methodology): “Some examples of frequently used metrics are randomness of area size [23, 32], boundary roughness [14, 11, 28, 32], and boundary area disorder [18].”
- Computer vision based method for real-time fire and flame detection (2006, unknown): “It is reported that non-fire contour recognition rate is 1.0 in (Liu and Ahuja, 2004) which corresponds to a false alarm rate of 0.”
- Convolutional Neural Networks Based Fire Detection in Surveillance Videos (2018, methodology): “The pixel-level methods [5], [6] are fast due to usage of pixel-wise features such as colors and flickers, however, their performance is not”