Fast neural learning vision system for crowd estimation at underground stations platform

Siu Yeung Cho, Tommy W.S. Chow

Research output: Journal PublicationArticlepeer-review

22 Citations (Scopus)

Abstract

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

Original languageEnglish
Pages (from-to)111-120
Number of pages10
JournalNeural Processing Letters
Volume10
Issue number2
DOIs
Publication statusPublished - 1999
Externally publishedYes

ASJC Scopus subject areas

  • Software
  • General Neuroscience
  • Computer Networks and Communications
  • Artificial Intelligence

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