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.
@article{arxiv.1706.03686,
title = {Image Crowd Counting Using Convolutional Neural Network and Markov Random Field},
author = {Kang Han and Wanggen Wan and Haiyan Yao and Li Hou},
journal= {arXiv preprint arXiv:1706.03686},
year = {2017}
}