A people-counting system using a hybrid RBF neural network

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

19 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)97-113
Journal / PublicationNeural Processing Letters
Volume18
Issue number2
Publication statusPublished - Oct 2003

Abstract

A people-counting system using hybrid RBF neural network is described. The proposed system is effective and flexible for the purpose of performing on-line people counting. Compared with other conventional approach, this system introduces a novel method for feature extraction. In this Letter, a new type of hybrid RBF network is developed to enhance the classification performance. The hybrid RBF based people-counting system is thoroughly compared with other approaches. Extensive and promising results were obtained and the analysis indicates that the proposed hybrid RBF based system provides excellent people- counting results in an open passage, A supervised clustering method is proposed for initialising the hybrid RBF network. In order to substantiate the introduction of the hybrid RBF and the proposed supervised clustering algorithm, test results on a vowel recognition benchmark data-set are also included in the Letter.

Research Area(s)

  • Feature extraction, Hybrid RBF network, Image segmentation, People counting, Radial basis function network, Supervised clustering