  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.
