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Estimation of Crowd Density in Public Areas Based on Neural Network
  • KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Estimation of Crowd Density in Public Areas Based on Neural Network

Vol. 6, No. 9, September 25, 2012
10.3837/tiis.2012.09.011, Download Paper (Free):

Abstract

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.


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Cite this article

[IEEE Style]
G. Kim, T. An and M. Kim, "Estimation of Crowd Density in Public Areas Based on Neural Network," KSII Transactions on Internet and Information Systems, vol. 6, no. 9, pp. 2170-2190, 2012. DOI: 10.3837/tiis.2012.09.011.

[ACM Style]
Gyujin Kim, Taeki An, and Moonhyun Kim. 2012. Estimation of Crowd Density in Public Areas Based on Neural Network. KSII Transactions on Internet and Information Systems, 6, 9, (2012), 2170-2190. DOI: 10.3837/tiis.2012.09.011.