A neural-based crowd estimation by hybrid global learning algorithm

Siu Yeung Cho; Tommy W.S. Chow; Chi Tat Leung

doi:10.1109/3477.775269

A neural-based crowd estimation by hybrid global learning algorithm

Siu Yeung Cho, Tommy W.S. Chow, Chi Tat Leung

Research output: Journal Publication › Article › peer-review

154 Citations (Scopus)

Abstract

A neural-based crowd estimation system for surveillance in complex scenes at underground station platform is presented. Estimation is carried out by extracting a set of significant features from sequences of images. Those feature indexes are modeled by a neural network to estimate the crowd density. The learning phase is based on our proposed hybrid of the least-squares and global search algorithms which are capable of providing the global search characteristic and fast convergence speed. Promising experimental results are obtained in terms of accuracy and real-time response capability to alert operators automatically.

Original language	English
Pages (from-to)	535-541
Number of pages	7
Journal	IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume	29
Issue number	4
DOIs	https://doi.org/10.1109/3477.775269
Publication status	Published - Aug 1999
Externally published	Yes

ASJC Scopus subject areas

Control and Systems Engineering
Software
Information Systems
Human-Computer Interaction
Computer Science Applications
Electrical and Electronic Engineering

Access to Document

10.1109/3477.775269

Cite this

@article{34783de034e24631b9a331d89d359a06,

title = "A neural-based crowd estimation by hybrid global learning algorithm",

abstract = "A neural-based crowd estimation system for surveillance in complex scenes at underground station platform is presented. Estimation is carried out by extracting a set of significant features from sequences of images. Those feature indexes are modeled by a neural network to estimate the crowd density. The learning phase is based on our proposed hybrid of the least-squares and global search algorithms which are capable of providing the global search characteristic and fast convergence speed. Promising experimental results are obtained in terms of accuracy and real-time response capability to alert operators automatically.",

author = "Cho, {Siu Yeung} and Chow, {Tommy W.S.} and Leung, {Chi Tat}",

year = "1999",

month = aug,

doi = "10.1109/3477.775269",

language = "English",

volume = "29",

pages = "535--541",

journal = "IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics",

issn = "1083-4419",

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

number = "4",

}

TY - JOUR

T1 - A neural-based crowd estimation by hybrid global learning algorithm

AU - Cho, Siu Yeung

AU - Chow, Tommy W.S.

AU - Leung, Chi Tat

PY - 1999/8

Y1 - 1999/8

N2 - A neural-based crowd estimation system for surveillance in complex scenes at underground station platform is presented. Estimation is carried out by extracting a set of significant features from sequences of images. Those feature indexes are modeled by a neural network to estimate the crowd density. The learning phase is based on our proposed hybrid of the least-squares and global search algorithms which are capable of providing the global search characteristic and fast convergence speed. Promising experimental results are obtained in terms of accuracy and real-time response capability to alert operators automatically.

AB - A neural-based crowd estimation system for surveillance in complex scenes at underground station platform is presented. Estimation is carried out by extracting a set of significant features from sequences of images. Those feature indexes are modeled by a neural network to estimate the crowd density. The learning phase is based on our proposed hybrid of the least-squares and global search algorithms which are capable of providing the global search characteristic and fast convergence speed. Promising experimental results are obtained in terms of accuracy and real-time response capability to alert operators automatically.

UR - http://www.scopus.com/inward/record.url?scp=0033177740&partnerID=8YFLogxK

U2 - 10.1109/3477.775269

DO - 10.1109/3477.775269

M3 - Article

C2 - 18252328

AN - SCOPUS:0033177740

SN - 1083-4419

VL - 29

SP - 535

EP - 541

JO - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

JF - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

IS - 4

ER -

A neural-based crowd estimation by hybrid global learning algorithm

Abstract

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this