A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Early Fire Detection on Video Using LBP and Spread Ascending of Smoke
This paper proposes a methodology for early fire detection based on visual smoke characteristics such as movement, color, gray tones and dynamic texture, i.e., diverse but representative and discriminant characteristics, as well as its ascending expansion, which is sequentially processed to find the candidate smoke regions. Thus, once a region with movement is detected, the pixels inside it that are smoke color are estimated to obtain a more detailed description of the smoke candidate region. Next, to increase the system efficiency and reduce false alarms, each region is characterized using the local binary pattern, which analyzes its texture and classifies it by means of a multi-layer perceptron. Finally, the ascending expansion of the candidate region is analyzed and those smoke regions that maintain or increase their ascending growth over a time span are considered as a smoke regions, and an alarm is triggered. Evaluations were performed using two different classifiers, namely multi-Layer perceptron and the support vector machine, with a standard database smoke video. Evaluation results show that the proposed system provides fire detection accuracy of between 97.85% and 99.83%.
Early Fire Detection on Video Using LBP and Spread Ascending of Smoke
This paper proposes a methodology for early fire detection based on visual smoke characteristics such as movement, color, gray tones and dynamic texture, i.e., diverse but representative and discriminant characteristics, as well as its ascending expansion, which is sequentially processed to find the candidate smoke regions. Thus, once a region with movement is detected, the pixels inside it that are smoke color are estimated to obtain a more detailed description of the smoke candidate region. Next, to increase the system efficiency and reduce false alarms, each region is characterized using the local binary pattern, which analyzes its texture and classifies it by means of a multi-layer perceptron. Finally, the ascending expansion of the candidate region is analyzed and those smoke regions that maintain or increase their ascending growth over a time span are considered as a smoke regions, and an alarm is triggered. Evaluations were performed using two different classifiers, namely multi-Layer perceptron and the support vector machine, with a standard database smoke video. Evaluation results show that the proposed system provides fire detection accuracy of between 97.85% and 99.83%.
Early Fire Detection on Video Using LBP and Spread Ascending of Smoke
Jesus Olivares-Mercado (author) / Karina Toscano-Medina (author) / Gabriel Sánchez-Perez (author) / Aldo Hernandez-Suarez (author) / Hector Perez-Meana (author) / Ana Lucila Sandoval Orozco (author) / Luis Javier García Villalba (author)
2019
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Video Fire Smoke Detection Using Motion and Color Features
| British Library Online Contents | 2010
Early Warning Fire Detection Using Laser Spot Smoke Sensors
| British Library Conference Proceedings | 1997
An effective four-stage smoke-detection algorithm using video images for early fire-alarm systems
| British Library Online Contents | 2011
Concept for Fire and smoke spread prevention in mines
| British Library Conference Proceedings | 2008