Estimating the crowding level with a neuro-fuzzy classifier

Massimo Boninsegna; Tarcisio Coianiz; Edmondo Trentin

doi:10.1117/12.269900

1 July 1997 Estimating the crowding level with a neuro-fuzzy classifier

Massimo Boninsegna, Tarcisio Coianiz, Edmondo Trentin

Author Affiliations +

Journal of Electronic Imaging, Vol. 6, Issue 3, (July 1997). https://doi.org/10.1117/12.269900

Abstract

This paper introduces a neuro-fuzzy system for the estimation of the crowding level in a scene. Monitoring the number of people present in a given indoor environment is a requirement in a variety of surveillance applications. In the present work, crowding has to be estimated from the image processing of visual scenes collected via a TV camera. A suitable preprocessing of the images, along with an ad hoc feature extraction process, is discussed. Estimation of the crowding level in the feature space is described in terms of a fuzzy decision rule, which relies on the membership of input patterns to a set of partially overlapping crowding classes, comprehensive of doubt classifications and outliers. A society of neural networks, either multilayer perceptrons or hyper radial basis functions, is trained to model individual class-membership functions. Integration of the neural nets within the fuzzy decision rule results in an overall neuro-fuzzy classifier. Important topics concerning the generalization ability, the robustness, the adaptivity and the performance evaluation of the system are explored. Experiments with real-world data were accomplished, comparing the present approach with statistical pattern recognition techniques, namely linear discriminant analysis and nearest neighbor. Experimental results validate the neuro-fuzzy approach to a large extent. The system is currently working successfully as a part of a monitoring system in the Dinegro underground station in Genoa, Italy.

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Massimo Boninsegna, Tarcisio Coianiz, and Edmondo Trentin "Estimating the crowding level with a neuro-fuzzy classifier," Journal of Electronic Imaging 6(3), (1 July 1997). https://doi.org/10.1117/12.269900

Published: 1 July 1997

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