You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.

The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.

For the reports in this repository we specifically note that

  • the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at http://www.ieee.org/web/publications/rights/copyrightpolicy.html)
  • the use of articles under ACM copyright is governed by the ACM copyright policy (available at http://www.acm.org/pubs/copyright_policy/)
  • technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required
  • in other cases, please contact the copyright owner for detailed information

By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.

If you are in doubt, feel free to contact webmaster@ide.mdh.se

Fire and smoke detection using wavelet analysis and disorder characteristics

Fulltext:


Authors:

Ali Rafiee, Reza Tavakoli, Reza Dianat, Sara Abbaspour

Publication Type:

Conference/Workshop Paper

Abstract

Fire and smoke monitoring systems are useful in different industry such as military, social security and economical. The recent methods for fire and smoke detection are used only motion and color characteristics thus many wrong alarms are happening and this is decrease the performance of the systems. This research presents a new method for fire and smoke detection through image processing. In this algorithm all objects in an image is considered and then check them to figure out which objects are smoke and fire. The color, motion and disorder are useful characteristics in fire and smoke detection algorithm. Smoke of fire will blur the whole or part of the images. Thus by processing of the video frames, different objects will detect. Due to evaluate the features of objects, the goal objects (fire and smoke) can be defined easily. Two-dimensional wavelet analysis is used in the presented method. The results of this research present the proposed features that can reduce the wrong alarms and increase the system performances.

Bibtex

@inproceedings{Rafiee3026,
author = {Ali Rafiee and Reza Tavakoli and Reza Dianat and Sara Abbaspour},
title = {Fire and smoke detection using wavelet analysis and disorder characteristics},
isbn = {978-1-61284-839-6},
editor = {IEEE},
month = {March},
year = {2011},
url = {http://www.es.mdu.se/publications/3026-}
}