Journal
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
Volume 43, Issue -, Pages 81-88Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engappai.2015.04.006
Keywords
Crowd density; Convolutional neural network; Neural networks
Categories
Funding
- National Natural Science Foundation of China [61375038]
- 973 National Basic Research Program of China [2010CB732501]
- Fundamental Research Funds for the Central University [ZYGX2012YB028, ZYGX2011X015]
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As an effective way for crowd control and management, crowd density estimation is an important research topic in artificial intelligence applications. Since the existing methods are hard to satisfy the accuracy and speed requirements of engineering applications, we propose to estimate crowd density by an optimized convolutional neural network (ConvNet). The contributions are twofold: first, convolutional neural network is first introduced for crowd density estimation. The estimation speed is significantly accelerated by removing some network connections according to the observation of the existence of similar feature maps. Second, a cascade of two ConvNet classifier has been designed, which improves both of the accuracy and speed. The method is tested on three data sets: PETS_2009, a Subway image sequence and a ground truth image sequence. Experiments confirm the good performance of the method on the same data sets compared with the state of the art works. (C) 2015 Elsevier Ltd. All rights reserved.
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