@inproceedings{d28fbaeb43ba46d5b22ed7fb5770e1af,
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",
note = "Publisher Copyright: {\textcopyright} 2016 Chinese Automation and Computing Society.; 22nd International Conference on Automation and Computing, ICAC 2016 ; Conference date: 07-09-2016 Through 08-09-2016",
year = "2016",
month = oct,
day = "20",
doi = "10.1109/IConAC.2016.7604963",
language = "English",
series = "2016 22nd International Conference on Automation and Computing, ICAC 2016: Tackling the New Challenges in Automation and Computing",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "462--466",
editor = "Jing Wang and Zhijie Xu",
booktitle = "2016 22nd International Conference on Automation and Computing, ICAC 2016",
}