1/2015 - 8 |
Computer Vision Based Measurement of Wildfire Smoke DynamicsBUGARIC, M. , JAKOVCEVIC, T. , STIPANICEV, D. |
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Author keywords
image motion analysis, computer vision, computer aided analysis, virtual reality, pattern analysis
References keywords
smoke(16), detection(14), fire(8), wildfire(6), visual(5), computational(5), video(4), spatial(4), image(4), forest(4)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2015-02-28
Volume 15, Issue 1, Year 2015, On page(s): 55 - 62
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2015.01008
Web of Science Accession Number: 000352158600008
SCOPUS ID: 84924804457
Abstract
This article presents a novel method for measurement of wildfire smoke dynamics based on computer vision and augmented reality techniques. The aspect of smoke dynamics is an important feature in video smoke detection that could distinguish smoke from visually similar phenomena. However, most of the existing smoke detection systems are not capable of measuring the real-world size of the detected smoke regions. Using computer vision and GIS-based augmented reality, we measure the real dimensions of smoke plumes, and observe the change in size over time. The measurements are performed on offline video data with known camera parameters and location. The observed data is analyzed in order to create a classifier that could be used to eliminate certain categories of false alarms induced by phenomena with different dynamics than smoke. We carried out an offline evaluation where we measured the improvement in the detection process achieved using the proposed smoke dynamics characteristics. The results show a significant increase in algorithm performance, especially in terms of reducing false alarms rate. From this it follows that the proposed method for measurement of smoke dynamics could be used to improve existing smoke detection algorithms, or taken into account when designing new ones. |
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[1] An Efficient Deep Learning Algorithm for Fire and Smoke Detection with Limited Data, NAMOZOV, A., CHO, Y. I., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 4, Volume 18, 2018.
Digital Object Identifier: 10.4316/AECE.2018.04015 [CrossRef] [Full text]
[2] 3d-reconstruction of destructive process models using remote sensing by a group of unmanned aerial vehicles, V, Sherstiuk, M, Zharikova, I, Dorovskaja, D, Chornyi, V, Romantsov, N, Kozub, V, Gusev, I, Sokol, Artificial Intelligence, ISSN 2710-1673, Issue jai2022.27(1), Volume 27, 2022.
Digital Object Identifier: 10.15407/jai2022.01.311 [CrossRef]
[3] Evaluation of Fire Intensity Based on Neural Networks in a Forest-Fire Monitoring System, Sherstjuk, Vladimir, Zharikova, Maryna, 2019 IEEE 39th International Conference on Electronics and Nanotechnology (ELNANO), ISBN 978-1-7281-2065-2, 2019.
Digital Object Identifier: 10.1109/ELNANO.2019.8783410 [CrossRef]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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