A neural-based crowd estimation by hybrid global learning algorithm

  • Authors:

  • Siu-Yeung Cho;T. W.S. Chow;Chi-Tat Leung

  • Affiliations:

  • Dept. of Electron. Eng., City Univ. of Hong Kong, Kowloon;-;-

  • Venue:
  • IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
  • Year:
  • 1999

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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