A People-Counting System Using a Hybrid RBF Neural Network

ArticleinNeural Processing Letters 18(2):97-113 · October 2003with17 Reads
Impact Factor: 1.45 · DOI: 10.1023/A:1026226617974 · Source: DBLP

    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 dataset are also included in the Letter.