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%.
Highlights
Earlier, faster and accurate fire detection is very important to safeguard human lives, wildlife, forests, etc., because it allows a timely response by emergency services
Smoke, which is the first symptom in flameless combustion in most fires, has various important characteristics that can be used for early fire detection: low temperature, movement, gray tones, dynamic texture, and ascending expansion
Two multilayer perceptron back propagation neural networks (MLP_BP) were trained with 1000 images that contain smoke and 1000 that contain different textures, segmented into square blocks of 48 pixels, while 840 images not used during training, 300 containing smoke and 540 not containing smoke, were used for testing
Summary
Faster and accurate fire detection is very important to safeguard human lives, wildlife, forests, etc., because it allows a timely response by emergency services. Intelligent and automated video surveillance systems, which have been a topic of active research and development during the last years, exploit those qualities for early fire detection These systems have important advantages over traditional systems, such as quick detection, not requiring smoke contact, can operate in open spaces or adverse air currents, remote sensing capabilities, and take advantage of the existing infrastructure in many buildings and public areas. These systems are based on visual fire detection techniques that use standard video surveillance cameras, together with sophisticated computational analysis algorithms that segment the images to describe and recognize the visual fire characteristics to determine their presence. This work focuses on the detection of smoke to activate the fire alarm
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
