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A neural-based crowd estimation by hybrid global learning algorithm

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3 Author(s)
Siu-Yeung Cho ; Dept. of Electron. Eng., City Univ. of Hong Kong, Kowloon, Hong Kong ; Chow, T.W.S. ; Chi-Tat Leung

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

Published in:

Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on  (Volume:29 ,  Issue: 4 )

Date of Publication:

Aug 1999

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