An approach to detect crowd panic behavior using flow-based feature

Yu Hao; Zhijie Xu; Jing Wang; Ying Liu; Jiulun Fan

doi:10.1109/IConAC.2016.7604963

An approach to detect crowd panic behavior using flow-based feature

Yu Hao, Zhijie Xu, Jing Wang, Ying Liu, Jiulun Fan

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

13 Citations (Scopus)

Abstract

With the purpose of achieving automated detection of crowd abnormal behavior in public, this paper discusses the category of typical crowd and individual behaviors and their patterns. Popular image features for abnormal behavior detection are also introduced, including global flow based features such as optical flow, and local spatio-temporal based features such as Spatio-temporal Volume (STV). After reviewing some relative abnormal behavior detection algorithms, a brand-new approach to detect crowd panic behavior has been proposed based on optical flow features in this paper. During the experiments, all panic behaviors are successfully detected. In the end, the future work to improve current approach has been discussed.

Original language	English
Title of host publication	22nd International Conference on Automation and Computing
Subtitle of host publication	Tackling the New Challenges in Automation and Computing
Editors	Zhijie Xu, Jing Wang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	462-466
Number of pages	5
ISBN (Electronic)	9781862181311
DOIs	https://doi.org/10.1109/IConAC.2016.7604963
Publication status	Published - 24 Oct 2016
Event	22nd International Conference on Automation and Computing - Colchester, United Kingdom Duration: 7 Sep 2016 → 8 Sep 2016 Conference number: 22

Conference

Conference	22nd International Conference on Automation and Computing
Abbreviated title	ICAC 2016
Country/Territory	United Kingdom
City	Colchester
Period	7/09/16 → 8/09/16

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10.1109/IConAC.2016.7604963Licence: Unspecified

http://eprints.hud.ac.uk/id/eprint/29079/Licence: Unspecified

Cite this

Hao, Y., Xu, Z., Wang, J., Liu, Y., & Fan, J. (2016). An approach to detect crowd panic behavior using flow-based feature. In Z. Xu, & J. Wang (Eds.), 22nd International Conference on Automation and Computing: Tackling the New Challenges in Automation and Computing (pp. 462-466). [7604963] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IConAC.2016.7604963

@inproceedings{b6b5defda8684a6d8d29b29d98bbdf18,

title = "An approach to detect crowd panic behavior using flow-based feature",

abstract = "With the purpose of achieving automated detection of crowd abnormal behavior in public, this paper discusses the category of typical crowd and individual behaviors and their patterns. Popular image features for abnormal behavior detection are also introduced, including global flow based features such as optical flow, and local spatio-temporal based features such as Spatio-temporal Volume (STV). After reviewing some relative abnormal behavior detection algorithms, a brand-new approach to detect crowd panic behavior has been proposed based on optical flow features in this paper. During the experiments, all panic behaviors are successfully detected. In the end, the future work to improve current approach has been discussed.",

keywords = "behavior detection, optical flow, video processing",

author = "Yu Hao and Zhijie Xu and Jing Wang and Ying Liu and Jiulun Fan",

year = "2016",

month = oct,

day = "24",

doi = "10.1109/IConAC.2016.7604963",

language = "English",

pages = "462--466",

editor = "Zhijie Xu and Jing Wang",

booktitle = "22nd International Conference on Automation and Computing",

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

address = "United States",

note = "22nd International Conference on Automation and Computing, ICAC 2016 ; Conference date: 07-09-2016 Through 08-09-2016",

}

Hao, Y, Xu, Z, Wang, J, Liu, Y & Fan, J 2016, An approach to detect crowd panic behavior using flow-based feature. in Z Xu & J Wang (eds), 22nd International Conference on Automation and Computing: Tackling the New Challenges in Automation and Computing., 7604963, Institute of Electrical and Electronics Engineers Inc., pp. 462-466, 22nd International Conference on Automation and Computing, Colchester, United Kingdom, 7/09/16. https://doi.org/10.1109/IConAC.2016.7604963

An approach to detect crowd panic behavior using flow-based feature. / Hao, Yu; Xu, Zhijie; Wang, Jing et al.

22nd International Conference on Automation and Computing: Tackling the New Challenges in Automation and Computing. ed. / Zhijie Xu; Jing Wang. Institute of Electrical and Electronics Engineers Inc., 2016. p. 462-466 7604963.

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

TY - GEN

T1 - An approach to detect crowd panic behavior using flow-based feature

AU - Hao, Yu

AU - Xu, Zhijie

AU - Wang, Jing

AU - Liu, Ying

AU - Fan, Jiulun

N1 - Conference code: 22

PY - 2016/10/24

Y1 - 2016/10/24

N2 - With the purpose of achieving automated detection of crowd abnormal behavior in public, this paper discusses the category of typical crowd and individual behaviors and their patterns. Popular image features for abnormal behavior detection are also introduced, including global flow based features such as optical flow, and local spatio-temporal based features such as Spatio-temporal Volume (STV). After reviewing some relative abnormal behavior detection algorithms, a brand-new approach to detect crowd panic behavior has been proposed based on optical flow features in this paper. During the experiments, all panic behaviors are successfully detected. In the end, the future work to improve current approach has been discussed.

AB - With the purpose of achieving automated detection of crowd abnormal behavior in public, this paper discusses the category of typical crowd and individual behaviors and their patterns. Popular image features for abnormal behavior detection are also introduced, including global flow based features such as optical flow, and local spatio-temporal based features such as Spatio-temporal Volume (STV). After reviewing some relative abnormal behavior detection algorithms, a brand-new approach to detect crowd panic behavior has been proposed based on optical flow features in this paper. During the experiments, all panic behaviors are successfully detected. In the end, the future work to improve current approach has been discussed.

KW - behavior detection

KW - optical flow

KW - video processing

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DO - 10.1109/IConAC.2016.7604963

M3 - Conference contribution

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BT - 22nd International Conference on Automation and Computing

A2 - Xu, Zhijie

A2 - Wang, Jing

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 22nd International Conference on Automation and Computing

Y2 - 7 September 2016 through 8 September 2016

ER -

An approach to detect crowd panic behavior using flow-based feature

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