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JACIII Vol.21 No.4 pp. 632-638
doi: 10.20965/jaciii.2017.p0632
(2017)

Paper:

Image Crowd Counting Using Convolutional Neural Network and Markov Random Field

Kang Han, Wanggen Wan, Haiyan Yao, and Li Hou

School of Communication and Information Engineering, Shanghai University
Institute of Smart City, Shanghai University
99 Shangda Road, BaoShan District, Shanghai 200444, China

Received:
January 25, 2017
Accepted:
May 10, 2017
Published:
July 20, 2017
Keywords:
crowd counting, convolutional neural network, Markov random field
Abstract

In this paper, we propose a method called Convolutional Neural Network-Markov Random Field (CNN-MRF) to estimate the crowd count in a still image. We first divide the dense crowd visible image into overlapping patches and then use a deep convolutional neural network to extract features from each patch image, followed by a fully connected neural network to regress the local patch crowd count. Since the local patches have overlapping portions, the crowd count of the adjacent patches has a high correlation. We use this correlation and the Markov random field to smooth the counting results of the local patches. Experiments show that our approach significantly outperforms the state-of-the-art methods on UCF and Shanghaitech crowd counting datasets.

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Last updated on Oct. 20, 2017