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Journal of Electrical and Computer Engineering
Volume 2017, Article ID 2580860, 12 pages
https://doi.org/10.1155/2017/2580860
Research Article

Crowd Density Estimation of Scenic Spots Based on Multifeature Ensemble Learning

1School of Electronic Information, Wuhan University, 129 Luoyu Road, Wuhan, Hubei 430072, China
2Shenzhen Institute of Wuhan University, Shenzhen, Guangdong 518057, China

Correspondence should be addressed to Hong Zheng; nc.ude.uhw@hz

Received 25 December 2016; Revised 30 March 2017; Accepted 8 May 2017; Published 8 June 2017

Academic Editor: Panajotis Agathoklis

Copyright © 2017 Xiaohang Xu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Estimating the crowd density of public territories, such as scenic spots, is of great importance for ensuring population safety and social stability. Due to problems in scenic spots such as illumination change, camera angle change, and pedestrian occlusion, current methods are unable to make accurate estimations. To deal with these problems, an ensemble learning (EL) method using support vector regression (SVR) is proposed in this study for crowd density estimation (CDE). The method first uses human head width as a reference to separate the foreground into multiple levels of blocks. Then it adopts the first-level SVR model to roughly predict the three features extracted from image blocks, including D-SIFT, ULBP, and GIST, and the prediction results are used as new features for the second-level SVR model for fine prediction. The prediction results of all image blocks are added for density estimation according to the crowd levels predefined for different scenes of scenic spots. Experimental results demonstrate that the proposed method can achieve a classification rate over 85% for multiple scenes of scenic spots, and it is an effective CDE method with strong adaptability.