Efficient video-equipped fire detection approach for automatic fire alarm systems

Myeongsu Kang; Truong Xuan Tung; Jong-Myon Kim

doi:10.1117/1.OE.52.1.017002

4 January 2013 Efficient video-equipped fire detection approach for automatic fire alarm systems

Myeongsu Kang, Truong Xuan Tung, Jong-Myon Kim

Author Affiliations +

Optical Engineering, Vol. 52, Issue 1, 017002 (January 2013). https://doi.org/10.1117/1.OE.52.1.017002

Abstract

This paper proposes an efficient four-stage approach that automatically detects fire using video capabilities. In the first stage, an approximate median method is used to detect video frame regions involving motion. In the second stage, a fuzzy c-means-based clustering algorithm is employed to extract candidate regions of fire from all of the movement-containing regions. In the third stage, a gray level co-occurrence matrix is used to extract texture parameters by tracking red-colored objects in the candidate regions. These texture features are, subsequently, used as inputs of a back-propagation neural network to distinguish between fire and nonfire. Experimental results indicate that the proposed four-stage approach outperforms other fire detection algorithms in terms of consistently increasing the accuracy of fire detection in both indoor and outdoor test videos.

Citation Download Citation

Myeongsu Kang, Truong Xuan Tung, and Jong-Myon Kim "Efficient video-equipped fire detection approach for automatic fire alarm systems," Optical Engineering 52(1), 017002 (4 January 2013). https://doi.org/10.1117/1.OE.52.1.017002

Published: 4 January 2013

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available