Fire detection in video sequences using a generic color model
By Turgay Çelik, Hasan Demirel. 2009
557 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/ef294dd96c2228c9e94337b4b9b50831cac66fc3
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
- Abolfazl Razi
- A. Cetin
- Fatemeh Afghah
- Sung Wook Baik
- B. U. Töreyin
- Jamil Ahmad
In Electrical and Electronic Engineering
- Khan Muhammad
- A. Cetin
- Paolo Bellavista
- Sung Wook Baik
- B. U. Töreyin
- Jamil Ahmad
- Kosmas Dimitropoulos
In Safety, Risk, Reliability and Quality
Citations in the corpus
- Aerial Imagery Pile burn detection using Deep Learning: the FLAME dataset (2020, methodology): “In the past, traditional image processing techniques utilized RGB channel comparison to detect different objects such as fire in frames or videos [37, 38, 39].”
- Video fire detection - Review (2013, methodology): “[60] and the smoke detection by Toreyin et al.”
- Efficient Deep CNN-Based Fire Detection and Localization in Video Surveillance Applications (2019, methodology): “Çelik and Demirel [19] explored the 88 YCbCr color space and presented a pixel classification method 89”
- HISTOGRAM-BASED SMOKE SEGMENTATION IN FOREST FIRE DETECTION SYSTEM (2009, unknown): “Different approaches and sensor inputs are used for the automatic detection of fire [2] [3], ranging from IR sensors to identify heat flux from the fire [4], light detection and ranging (LIDAR) systems [5] [6] that measure the laser light backscattered by the smoke particles, detection of smoke on…”