Image Crowd Counting Using Convolutional Neural Network and Markov Random Field
This addresses crowd counting for surveillance and public safety, but it is incremental as it builds on existing CNN and MRF techniques.
The paper tackles the problem of estimating crowd counts in still images by proposing a CNN-MRF method that combines convolutional neural networks with Markov random fields for smoothing, achieving significant improvements over state-of-the-art methods on UCF and Shanghaitech datasets.
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.