Flame detection in video using hidden Markov models

B. Uǧur Töreyin; Yiǧithan Dedeoǧlu; A. Enis Çetin

doi:10.1109/ICIP.2005.1530284

Flame detection in video using hidden Markov models

B. Uǧur Töreyin^*, Yiǧithan Dedeoǧlu, A. Enis Çetin

^*Corresponding author for this work

Bilkent University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

166 Citations (Scopus)

Abstract

This paper proposes a novel method to detect flames in video by processing the data generated by an ordinary camera monitoring a scene. In addition to ordinary motion and color clues, flame flicker process is also detected by using a hidden Markov model. Markov models representing the flame and flame colored ordinary moving objects are used to distinguish flame flicker process from motion of flame colored moving objects. Spatial color variations in flame are also evaluated by the same Markov models, as well. These clues are combined to reach a final decision. False alarms due to ordinary motion of flame colored moving objects are greatly reduced when compared to the existing video based fire detection systems.

Original language	English
Title of host publication	IEEE International Conference on Image Processing 2005, ICIP 2005
Pages	1230-1233
Number of pages	4
DOIs	https://doi.org/10.1109/ICIP.2005.1530284
Publication status	Published - 2005
Externally published	Yes
Event	IEEE International Conference on Image Processing 2005, ICIP 2005 - Genova, Italy Duration: 11 Sept 2005 → 14 Sept 2005

Publication series

Name	Proceedings - International Conference on Image Processing, ICIP
Volume	2
ISSN (Print)	1522-4880

Conference

Conference	IEEE International Conference on Image Processing 2005, ICIP 2005
Country/Territory	Italy
City	Genova
Period	11/09/05 → 14/09/05

Access to Document

10.1109/ICIP.2005.1530284

Cite this

@inproceedings{3be8a55287104903a44e909b1752a29c,

title = "Flame detection in video using hidden Markov models",

abstract = "This paper proposes a novel method to detect flames in video by processing the data generated by an ordinary camera monitoring a scene. In addition to ordinary motion and color clues, flame flicker process is also detected by using a hidden Markov model. Markov models representing the flame and flame colored ordinary moving objects are used to distinguish flame flicker process from motion of flame colored moving objects. Spatial color variations in flame are also evaluated by the same Markov models, as well. These clues are combined to reach a final decision. False alarms due to ordinary motion of flame colored moving objects are greatly reduced when compared to the existing video based fire detection systems.",

author = "T{\"o}reyin, {B. Uǧur} and Yiǧithan Dedeoǧlu and {\c C}etin, {A. Enis}",

year = "2005",

doi = "10.1109/ICIP.2005.1530284",

language = "English",

isbn = "0780391349",

series = "Proceedings - International Conference on Image Processing, ICIP",

pages = "1230--1233",

booktitle = "IEEE International Conference on Image Processing 2005, ICIP 2005",

note = "IEEE International Conference on Image Processing 2005, ICIP 2005 ; Conference date: 11-09-2005 Through 14-09-2005",

}

Töreyin, BU, Dedeoǧlu, Y & Çetin, AE 2005, Flame detection in video using hidden Markov models. in IEEE International Conference on Image Processing 2005, ICIP 2005., 1530284, Proceedings - International Conference on Image Processing, ICIP, vol. 2, pp. 1230-1233, IEEE International Conference on Image Processing 2005, ICIP 2005, Genova, Italy, 11/09/05. https://doi.org/10.1109/ICIP.2005.1530284

Flame detection in video using hidden Markov models. / Töreyin, B. Uǧur; Dedeoǧlu, Yiǧithan; Çetin, A. Enis.
IEEE International Conference on Image Processing 2005, ICIP 2005. 2005. p. 1230-1233 1530284 (Proceedings - International Conference on Image Processing, ICIP; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Flame detection in video using hidden Markov models

AU - Töreyin, B. Uǧur

AU - Dedeoǧlu, Yiǧithan

AU - Çetin, A. Enis

PY - 2005

Y1 - 2005

N2 - This paper proposes a novel method to detect flames in video by processing the data generated by an ordinary camera monitoring a scene. In addition to ordinary motion and color clues, flame flicker process is also detected by using a hidden Markov model. Markov models representing the flame and flame colored ordinary moving objects are used to distinguish flame flicker process from motion of flame colored moving objects. Spatial color variations in flame are also evaluated by the same Markov models, as well. These clues are combined to reach a final decision. False alarms due to ordinary motion of flame colored moving objects are greatly reduced when compared to the existing video based fire detection systems.

AB - This paper proposes a novel method to detect flames in video by processing the data generated by an ordinary camera monitoring a scene. In addition to ordinary motion and color clues, flame flicker process is also detected by using a hidden Markov model. Markov models representing the flame and flame colored ordinary moving objects are used to distinguish flame flicker process from motion of flame colored moving objects. Spatial color variations in flame are also evaluated by the same Markov models, as well. These clues are combined to reach a final decision. False alarms due to ordinary motion of flame colored moving objects are greatly reduced when compared to the existing video based fire detection systems.

UR - http://www.scopus.com/inward/record.url?scp=33749596594&partnerID=8YFLogxK

U2 - 10.1109/ICIP.2005.1530284

DO - 10.1109/ICIP.2005.1530284

M3 - Conference contribution

AN - SCOPUS:33749596594

SN - 0780391349

SN - 9780780391345

T3 - Proceedings - International Conference on Image Processing, ICIP

SP - 1230

EP - 1233

BT - IEEE International Conference on Image Processing 2005, ICIP 2005

T2 - IEEE International Conference on Image Processing 2005, ICIP 2005

Y2 - 11 September 2005 through 14 September 2005

ER -

Flame detection in video using hidden Markov models

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Cite this