Estimation of Crowd Density in Public Areas Based on Neural Network
Gyujin Kim · KSII Transactions on Internet and Information Systems · 2012
There are nowadays strong demands for intelligent surveillance systems, which can infer or understand more complex behavior.The application of crowd density estimation methods could lead to a better understanding of crowd behavior, improved design of the built environment, and increased pedestrian safety.In this paper, we propose a new crowd density estimation method, which aims at estimating not only a moving crowd, but also a stationary crowd, using images captured from surveillance cameras situated in various public locations.The crowd density of the moving people is measured, based on the moving area during a specified time period.The moving area is defined as the area where the magnitude of the accumulated optical flow exceeds a predefined threshold.In contrast, the stationary crowd density is estimated from the coarseness of textures, under the assumption that each person can be regarded as a textural unit.A multilayer neural network is designed, to classify crowd density levels into 5 classes.Finally, the proposed method is experimented with PETS 2009 and the platform of Gangnam subway station image sequences.