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

156 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

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10.1109/3477.775269

Cite this

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A neural-based crowd estimation by hybrid global learning algorithm

Abstract

ASJC Scopus subject areas

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