| 1/2015 - 8 |
Computer Vision Based Measurement of Wildfire Smoke DynamicsBUGARIC, M.   , JAKOVCEVIC, T. , STIPANICEV, D. |
Extra paper information in    |
| Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science |
Download PDF  (882 KB) | Citation | Downloads: 996 | Views: 4,001 |
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. |
| References | | | Cited By |
Web of Science® Times Cited: 5 [View]
View record in Web of Science® [View]
View Related Records® [View]
Updated 2 days, 18 hours ago
SCOPUS® Times Cited: 6
View record in SCOPUS® [Free preview]
View citations in SCOPUS® [Free preview]
Updated 2 days, 18 hours ago
[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]
Updated 2 days, 18 hours ago
Disclaimer: All information displayed above was retrieved by using remote connections to respective databases. For the best user experience, we update all data by using background processes, and use caches in order to reduce the load on the servers we retrieve the information from. As we have no control on the availability of the database servers and sometimes the Internet connectivity may be affected, we do not guarantee the information is correct or complete. For the most accurate data, please always consult the database sites directly. Some external links require authentication or an institutional subscription.
Web of Science® is a registered trademark of Clarivate Analytics, Scopus® is a registered trademark of Elsevier B.V., other product names, company names, brand names, trademarks and logos are the property of their respective owners.
Copyright ©2001-2024
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
