Home| Contact Us| New Journals| Browse Journals| Journal Prices| For Authors|

Print ISSN: 0976-9005
Online ISSN:
0976-9013


  About JIC
  DLINE Portal Home
Home
Aims & Scope
Editorial Board
Current Issue
Next Issue
Previous Issue
Sample Issue
Upcoming Conferences
Self-archiving policy
Alert Services
Be a Reviewer
Publisher
Paper Submission
Subscription
Contact us
 
  How To Order
  Order Online
Price Information
Request for Complimentary
Print Copy
 
  For Authors
  Guidelines for Contributors
Online Submission
Call for Papers
Author Rights
 
 
RELATED JOURNALS
Journal of Digital Information Management (JDIM)
International Journal of Computational Linguistics Research (IJCL)
International Journal of Web Application (IJWA)

 

 
Journal of Intelligent Computing
 

A System Based on Object Recognition Benefits for Fire Detection
Maria Pavlova
Technical University of Sofia 8 Kl. Ohridski Blvd Sofia 1000, Bulgaria
Abstract: In this work we have addressed the fire recognition issue. We have introduced a framework for fire recognition field in this paper. Using a snapshot from unmanned aerial vehicle, we recorded the visuals of the surrounding. This system enables the capturing of fire captured on the visuals and try to reduce the time for detecting fire. This pattern is supported by an open library which identifies the fire event. We further strengthen the paper with experimental evaluation.
Keywords: Object Recognition, OpenCV, Fire Recognition, Machine Learning A System Based on Object Recognition Benefits for Fire Detection
DOI:https://doi.org/10.6025/jic/2020/11/4/125-132
Full_Text   PDF 1.08 MB   Download:   324  times
References:

[1] https://doi.org/10.3182/20050703-6-CZ-1902.01380.
[2] https://www.tandfonline.com/doi/abs/10.1080/014311699212290
[3] Viola, P., Jones, M. (2001). Rapid object detection using a Boosted Cascade of Simple Features, ACCVPR, 2001, https://www.cs.cmu.edu/~efros/courses/LBMV07/Papers/violacvpr-01.pdf
[4] Keysers, D., Deselaers, T., Ney, H. (2004). Pixel-to-Pixel Matching for Image Recognition Using Hungarian Graph Matching. In: Rasmussen C.E., Bülthoff H. H., Schölkopf B., Giese M.A. (eds) Pattern Recognition. DAGM 2004. Lecture Notes in Computer Science, 3175. Springer, Berlin, Heidelberg
[5] Rainer Lienhart., Jochen Maydt. (2002). An Extended Set of Haar-like Features for Rapid Object Detection. IEEE ICIP 2002, Volume 1, p 900-903, September 2002. This paper, as well as the extended technical report, can be retrieved at http://www.multimediacomputing. de/mediawiki//images/5/52/MRL-TR-May02-revised-Dec02.pdf
[6] https://docs.opencv.org/2.4/modules/objdetect/doc/cascade_classification.html?highlight=cascadeclassifier#lienhart02
[7] https://pythonprogramming.net/haar-cascade-object-detectionpython-opencv-tutorial/, 2017
[8] Hao Wu., Deyang Wu., Jinsong Zhao. (2019). An intelligent fire detection approach through cameras based on computer vision methods, Process Safety and Environmental Protection, Volume 127, 2019, Pages 245-256,ISSN 0957-5820, https://doi.org/10.1016/j.psep.2019.05.016
[9] Mahdi Hashemzadeh., Alireza Zademehdi. (2019). Fire detection for video surveillance applications using ICA K-medoidsbased color model and efficient spatio-temporal visual features, Expert Systems with Applications, Volume 130, 2019, Pages 60-78, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2019.04.019.


Home | Aim & Scope | Editorial Board | Author Guidelines | Publisher | Subscription | Previous Issue | Contact Us |Upcoming Conferences|Sample Issues|Library Recommendation Form|

 

Copyright © 2011 dline.info