Crowd Counting using DMCNN

To estimate the crowd density map and count the crowd from a single image accurately is always a challenging task. With arbitrary perspective and random crowd density, occlusions, appearance variations and perspective distortions may occur. Some of current crowd counting methods are based on image cropping. And some popular deep learning models are difficult to optimize. In this paper, we propose a Dilated Multi-column Convolutional Neural Network architecture for crowd density estimation in still images improved from the MCNN model [1]. We also use the dilated layer and optimize the loss function to get better accuracy. The DMCNN model is lightweight, easy to train and has better fitting ability. Meanwhile the architecture is an end-to-end system and robust for images with different perspective or crowd density. Furthermore, the ground truth (density map) is generated based on our Perspective-Adaptive Gaussian Kernels which can better represent the heads of pedestrians. We conduct experiments on the WorldExpo'10 dataset, the ShanghaiTech dataset, the UCF_CC_50 dataset, and the mall dataset. The results show that our method achieves better estimation and is convenient to utilize. Our DMCNN model has a good practical application prospect.