Abstract
A neural learning-based crowd estimation system for surveillance in complex scenes at the platform of underground stations is presented. Estimation is carried out by extracting a set of significant features from the sequences of images. Feature indices are modeled by the neural networks to estimate the crowd density. The learning phase is based on our proposed hybrid algorithms which are capable of providing the global search characteristic and fast convergence speed. Promising experimental results were obtained in terms of estimation accuracy and real-time response capability to alert the operators automatically.
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Cho, SY., Chow, T.W.S. A Fast Neural Learning Vision System for Crowd Estimation at Underground Stations Platform. Neural Processing Letters 10, 111–120 (1999). https://doi.org/10.1023/A:1018781301409
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DOI: https://doi.org/10.1023/A:1018781301409