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
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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