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Title: A vision-based approach to fire detection
Authors: Gomes, P.
Santana, P.
Barata, J.
Keywords: Vision systems
Fire detection
Smart cameras
Computer vision
Object detection
Object tracking
Issue Date: 2014
Publisher: InTech
Abstract: This paper presents a vision-based method for fire detection from fixed surveillance smart cameras. The method integrates several well-known techniques properly adapted to cope with the challenges related to the actual deployment of the vision system. Concretely, background subtraction is performed with a context-based learning mechanism so as to attain higher accuracy and robustness. The computational cost of a frequency analysis of potential fire regions is reduced by means of focusing its operation with an attentive mechanism. For fast discrimination between fire regions and fire-coloured moving objects, a new colour-based model of fire’s appearance and a new wavelet-based model of fire’s frequency signature are proposed. To reduce the false alarm rate due to the presence of fire-coloured moving objects, the category and behaviour of each moving object is taken into account in the decision-making. To estimate the expected object’s size in the image plane and to generate geo-referenced alarms, the camera-world mapping is approximated with a GPS-based calibration process. Experimental results demonstrate the ability of the proposed method to detect fires with an average success rate of 93.1 % at a processing rate of 10 Hz, which is often sufficient for real-life applications.
Description: WOS:000341810500001 (Nº de Acesso Web of Science)
Peer reviewed: Sim
ISSN: 1729-8806
Publisher version: The definitive version is available at:
Appears in Collections:CTI-RI - Artigos em revistas científicas internacionais com arbitragem científica

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