Early Fire Detection on Video Using LBP and Spread Ascending of Smoke

Jesus Olivares-Mercado, Karina Toscano-Medina, Gabriel Sánchez-Perez, Aldo Hernandez-Suarez, Hector Perez-Meana, Ana Lucila Sandoval Orozco and Luis Javier García Villalba
Additional contact information
Jesus Olivares-Mercado: Instituto Politecnico Nacional, ESIME Culhuacan, Mexico City 04440, Mexico
Karina Toscano-Medina: Instituto Politecnico Nacional, ESIME Culhuacan, Mexico City 04440, Mexico
Gabriel Sánchez-Perez: Instituto Politecnico Nacional, ESIME Culhuacan, Mexico City 04440, Mexico
Aldo Hernandez-Suarez: Instituto Politecnico Nacional, ESIME Culhuacan, Mexico City 04440, Mexico
Hector Perez-Meana: Instituto Politecnico Nacional, ESIME Culhuacan, Mexico City 04440, Mexico
Ana Lucila Sandoval Orozco: Group of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Computer Science and Engineering, Office 431, Universidad Complutense de Madrid (UCM), Calle Profesor José García Santesmases, 9, Ciudad Universitaria, 28040 Madrid, Spain
Luis Javier García Villalba: Group of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Computer Science and Engineering, Office 431, Universidad Complutense de Madrid (UCM), Calle Profesor José García Santesmases, 9, Ciudad Universitaria, 28040 Madrid, Spain

Sustainability, 2019, vol. 11, issue 12, 1-16

Abstract: 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%.

Keywords: smoke detection; Multi-Layer Perceptron; Artificial Neural Network; Local Binary Pattern; Support Vector Machines (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/11/12/3261/pdf (application/pdf)
https://www.mdpi.com/2071-1050/11/12/3261/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:11:y:2019:i:12:p:3261-:d:239362

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().