Journal of Robotics, Networking and Artificial Life

Volume 4, Issue 1, June 2017, Pages 77 - 80

A Method of Detecting Abnormal Crowd Behavior Events Applied in Air Patrol Robot

Authors
Huailin Zhao, Shunzhou Wang, Shifang Xu, Yani Zhang, Masanori Sugisaka
Corresponding Author
Huailin Zhao
Available Online 1 June 2017.
DOI
10.2991/jrnal.2017.4.1.17How to use a DOI?
Keywords
Intelligent Surveillance, Gaussian Process Regression, Air Patrol Robot
Abstract

When the ground or air patrol robot monitors a certain area, one of the important intelligent functions is to monitoring and alerting unusual event of the monitored area. This paper proposes a method via the number of people in the monitored area to detect the environment is safety or not. We use mature methods to formulate the counting problem and the number of people in each frame can be calculated with Gaussian Process Regression model. We suppose that this application may provides an important technical support for enhancing the patrol robot monitoring effect.

Copyright
© 2013, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
Journal of Robotics, Networking and Artificial Life
Volume-Issue
4 - 1
Pages
77 - 80
Publication Date
2017/06/01
ISSN (Online)
2352-6386
ISSN (Print)
2405-9021
DOI
10.2991/jrnal.2017.4.1.17How to use a DOI?
Copyright
© 2013, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Huailin Zhao
AU  - Shunzhou Wang
AU  - Shifang Xu
AU  - Yani Zhang
AU  - Masanori Sugisaka
PY  - 2017
DA  - 2017/06/01
TI  - A Method of Detecting Abnormal Crowd Behavior Events Applied in Air Patrol Robot
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 77
EP  - 80
VL  - 4
IS  - 1
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.2017.4.1.17
DO  - 10.2991/jrnal.2017.4.1.17
ID  - Zhao2017
ER  -