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Sensors 2018, 18(11), 3780;

A Video Based Fire Smoke Detection Using Robust AdaBoost

1,2,* and 3
School of Automation, Southeast University, Nanjing 210096, China
Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing 210096, China
Department of Electrical and Computer Engineering, University of Calgary, 2500 University Dr N.W., Calgary, AB T2N 1N4, Canada
Author to whom correspondence should be addressed.
Received: 26 September 2018 / Revised: 26 October 2018 / Accepted: 31 October 2018 / Published: 5 November 2018
(This article belongs to the Section Intelligent Sensors)
Full-Text   |   PDF [1294 KB, uploaded 5 November 2018]   |  


This work considers using camera sensors to detect fire smoke. Static features including texture, wavelet, color, edge orientation histogram, irregularity, and dynamic features including motion direction, change of motion direction and motion speed, are extracted from fire smoke to train and test with different combinations. A robust AdaBoost (RAB) classifier is proposed to improve training and classification accuracy. Extensive experiments on well known challenging datasets and application for fire smoke detection demonstrate that the proposed fire smoke detector leads to a satisfactory performance. View Full-Text
Keywords: fire smoke detection; video based; feature extraction; robust AdaBoost; classifier fire smoke detection; video based; feature extraction; robust AdaBoost; classifier

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Wu, X.; Lu, X.; Leung, H. A Video Based Fire Smoke Detection Using Robust AdaBoost. Sensors 2018, 18, 3780.

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