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Abstract—A neural-based crowd estimation system for surveillance incomplex scenes at underground station platform is presented. Estimationis carried out by extracting a set of significant features from sequencesof images. Those feature indexes are modeled by a neural network toestimate the crowd density. The learning phase is based on our proposedhybrid of the least-squares and global search algorithms which arecapable of providing the global search characteristic and fast convergencespeed. Prom
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