@inproceedings{edb1efde88724fd48a60f536f5555d2d,
title = "Campus Bullying Detecting Algorithm Based on Surveillance Video",
abstract = "In recent years, more and more violent events are taking place in campus life. Campus bullying prevention is already the focus of current education. This paper proposes a campus bullying detecting algorithm based on surveillance video. It can actively monitor whether students are being bullied on campus. The authors use Openpose to extract bone information from video. According to the coordinate information of bone points, they extract static and dynamic features. Support vector machine (SVM) is used to classify different actions. The recognition accuracy of the classification model is 88.57\%. In this way, the campus surveillance camera is able to realize real-time monitoring of bullying behavior. It is conducive to the construction of a harmonious campus environment.",
keywords = "Campus bullying, Openpose, Support vector machine, bone points",
author = "Liang Ye and Susu Yan and Tian Han and Tapio Sepp{\"a}nen and Esko Alasaarela",
note = "Publisher Copyright: {\textcopyright} 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.; 2nd EAI International Conference on Artificial Intelligence for Communications and Networks, AICON 2020 ; Conference date: 19-12-2020 Through 20-12-2020",
year = "2021",
doi = "10.1007/978-3-030-69066-3\_27",
language = "英语",
isbn = "9783030690656",
series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "309--315",
editor = "Shuo Shi and Liang Ye and Yu Zhang",
booktitle = "Artificial Intelligence for Communications and Networks - 2nd EAI International Conference, AICON 2020, Proceedings",
address = "德国",
}