Industrial neural vision system for underground railway station platform surveillance |
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| Affiliation: | 1. ChongQing College of Electronic Engineering, ChongQing, China;2. School of Management, Wuhan Donghu University, Wuhan, China;1. Mathematical Institute, University of Oxford, Radcliffe Observatory Quarter, Woodstock Road, Oxford OX2 6GG, United Kingdom;2. Department of Mathematics, University of Haifa, Mount Carmel, Haifa, 3498838 Israel;1. Department of Civil and Environmental Engineering, University of Alberta, 9211 116 Street, Edmonton T6G 1H9, Canada;2. Landmark Group of Builders, #301, 1103 95 Street SW, Edmonton T6E 5J4, Canada |
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| Abstract: | An industrial neural network based crowd monitoring system for surveillance at underground station platforms is presented. The developed system was thoroughly off-line tested by video images obtained from the underground station platform at Hong Kong. The developed system enables the density level of crowd to be automatically estimated. Crowd estimation is carried out by extracting a set of significant features from sequence of video images. The extracted features are modelled by a neural network for estimating the level of crowd density. The learning process is based upon an efficient hybrid type global learning algorithms, which are capable of providing good learning performance. Very promising results were obtained in terms of estimation accuracy and real-time response capability to alert the operators automatically. |
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