Fire Detection in H.264 Compressed Video

Murat Muhammet Savci; Yasin Yildirim; Gorkem Saygili; Behcet Uǧur Töreyin

doi:10.1109/ICASSP.2019.8683666

Fire Detection in H.264 Compressed Video

Murat Muhammet Savci, Yasin Yildirim, Gorkem Saygili, Behcet Uǧur Töreyin

Informatics Institute

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

7 Citations (Scopus)

Abstract

In this paper, we propose a compressed domain fire detection algorithm using macroblock types and Markov Model in H.264 video. Compressed domain method does not require decoding to pixel domain, instead a syntax parser extracts syntax elements which are only available in compressed domain. Our method extracts only macroblock type and corresponding macroblock address information. Markov model with fire and non-fire models are evaluated using offline-trained data. Our experiments show that the algorithm is able to detect and identify fire event in compressed domain successfully, despite a small chunk of data is used in the process.

Original language	English
Title of host publication	2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	8310-8314
Number of pages	5
ISBN (Electronic)	9781479981311
DOIs	https://doi.org/10.1109/ICASSP.2019.8683666
Publication status	Published - May 2019
Event	44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom Duration: 12 May 2019 → 17 May 2019

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2019-May
ISSN (Print)	1520-6149

Conference

Conference	44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
Country/Territory	United Kingdom
City	Brighton
Period	12/05/19 → 17/05/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

H.264/AVC
compressed domain
fire detection
mac-roblock type

Access to Document

10.1109/ICASSP.2019.8683666

Cite this

Savci, M. M., Yildirim, Y., Saygili, G., & Töreyin, B. U. (2019). Fire Detection in H.264 Compressed Video. In 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings (pp. 8310-8314). Article 8683666 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2019-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2019.8683666

Savci, Murat Muhammet ; Yildirim, Yasin ; Saygili, Gorkem et al. / Fire Detection in H.264 Compressed Video. 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 8310-8314 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

@inproceedings{10e6eb4f950449539f914cb8aff46c93,

title = "Fire Detection in H.264 Compressed Video",

abstract = "In this paper, we propose a compressed domain fire detection algorithm using macroblock types and Markov Model in H.264 video. Compressed domain method does not require decoding to pixel domain, instead a syntax parser extracts syntax elements which are only available in compressed domain. Our method extracts only macroblock type and corresponding macroblock address information. Markov model with fire and non-fire models are evaluated using offline-trained data. Our experiments show that the algorithm is able to detect and identify fire event in compressed domain successfully, despite a small chunk of data is used in the process.",

keywords = "H.264/AVC, compressed domain, fire detection, mac-roblock type",

author = "Savci, \{Murat Muhammet\} and Yasin Yildirim and Gorkem Saygili and T{\"o}reyin, \{Behcet Uǧur\}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 ; Conference date: 12-05-2019 Through 17-05-2019",

year = "2019",

month = may,

doi = "10.1109/ICASSP.2019.8683666",

language = "English",

series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings",

address = "United States",

}

Savci, MM, Yildirim, Y, Saygili, G & Töreyin, BU 2019, Fire Detection in H.264 Compressed Video. in 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings., 8683666, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2019-May, Institute of Electrical and Electronics Engineers Inc., pp. 8310-8314, 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019, Brighton, United Kingdom, 12/05/19. https://doi.org/10.1109/ICASSP.2019.8683666

Fire Detection in H.264 Compressed Video. / Savci, Murat Muhammet; Yildirim, Yasin; Saygili, Gorkem et al.
2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 8310-8314 8683666 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2019-May).

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

TY - GEN

T1 - Fire Detection in H.264 Compressed Video

AU - Savci, Murat Muhammet

AU - Yildirim, Yasin

AU - Saygili, Gorkem

AU - Töreyin, Behcet Uǧur

PY - 2019/5

Y1 - 2019/5

N2 - In this paper, we propose a compressed domain fire detection algorithm using macroblock types and Markov Model in H.264 video. Compressed domain method does not require decoding to pixel domain, instead a syntax parser extracts syntax elements which are only available in compressed domain. Our method extracts only macroblock type and corresponding macroblock address information. Markov model with fire and non-fire models are evaluated using offline-trained data. Our experiments show that the algorithm is able to detect and identify fire event in compressed domain successfully, despite a small chunk of data is used in the process.

AB - In this paper, we propose a compressed domain fire detection algorithm using macroblock types and Markov Model in H.264 video. Compressed domain method does not require decoding to pixel domain, instead a syntax parser extracts syntax elements which are only available in compressed domain. Our method extracts only macroblock type and corresponding macroblock address information. Markov model with fire and non-fire models are evaluated using offline-trained data. Our experiments show that the algorithm is able to detect and identify fire event in compressed domain successfully, despite a small chunk of data is used in the process.

KW - H.264/AVC

KW - compressed domain

KW - fire detection

KW - mac-roblock type

UR - https://www.scopus.com/pages/publications/85068956958

U2 - 10.1109/ICASSP.2019.8683666

DO - 10.1109/ICASSP.2019.8683666

M3 - Conference contribution

AN - SCOPUS:85068956958

T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

SP - 8310

EP - 8314

BT - 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019

Y2 - 12 May 2019 through 17 May 2019

ER -

Savci MM, Yildirim Y, Saygili G, Töreyin BU. Fire Detection in H.264 Compressed Video. In 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. p. 8310-8314. 8683666. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP.2019.8683666

Fire Detection in H.264 Compressed Video

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Keywords

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