A fuzzy classifier for visual crowding estimates (original) (raw)

Proceedings of International Conference on Neural Networks (ICNN'96), 1996

Abstract

A trainable vision-based system is presented, which is able to perform reliable, real time estimates of the crowding level present on the platforms of underground stations. Taking as input standard the B/W images of the scene, a classification of the crowding level is performed in terms of five qualitative crowding classes, ranging from no people to overcrowding. Visual feature extraction

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