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 › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Pages (from-to) | 97-113 |
| Journal / Publication | Neural Processing Letters |
| Volume | 18 |
| Issue number | 2 |
| Publication status | Published - Oct 2003 |
Link(s)
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
Citation Format(s)
A people-counting system using a hybrid RBF neural network. / Huang, D.; Chow, Tommy W.S.
In: Neural Processing Letters, Vol. 18, No. 2, 10.2003, p. 97-113.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
