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



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Olivares Mercado, Jesus and Toscano Medina, Karina and Sánchez Perez, Gabriel and Hernandez Suarez, Aldo and Perez Meana, Hector and Sandoval Orozco, Ana Lucila and García Villalba, Luis Javier (2019) Early Fire Detection on Video Using LBP and Spread Ascending of Smoke. Sustainability, 11 (12). p. 3261. ISSN 2071-1050

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Official URL: https://doi.org/10.3390/su11123261


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%.

Item Type:Article
Uncontrolled Keywords:smoke detection; Multi-Layer Perceptron; Artificial Neural Network; Local Binary Pattern; Support Vector Machines
Subjects:Sciences > Computer science > Artificial intelligence
Sciences > Computer science > Networks
ID Code:68485
Deposited On:03 Nov 2021 11:57
Last Modified:03 Nov 2021 11:57

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