Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/43260
Title: Industrial neural vision system for underground railway station platform surveillance
Authors: Chow, TWS
Cho, SY
Keywords: Neural networks
Crowd estimation
Underground station platform
Hybrid global learning algorithm
Issue Date: 2002
Publisher: Elsevier
Source: Advanced engineering informatics, 2002, v. 16, no. 1, p. 73-83 How to cite?
Journal: Advanced engineering informatics 
Abstract: An industrial neural network based crowd monitoring system for surveillance at underground station platforms is presented. The developed system was thoroughly off-line tested by video images obtained from the underground station platform at Hong Kong. The developed system enables the density level of crowd to be automatically estimated. Crowd estimation is carried out by extracting a set of significant features from sequence of video images. The extracted features are modelled by a neural network for estimating the level of crowd density. The learning process is based upon an efficient hybrid type global learning algorithms, which are capable of providing good learning performance. Very promising results were obtained in terms of estimation accuracy and real-time response capability to alert the operators automatically.
URI: http://hdl.handle.net/10397/43260
ISSN: 1474-0346
DOI: 10.1016/S1474-0346(01)00002-7
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

WEB OF SCIENCETM
Citations

8
Last Week
0
Last month
Citations as of Mar 21, 2017

Page view(s)

7
Last Week
0
Last month
Checked on Mar 26, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.