Crowd Counting via Multi-Column Deconvolution Networks

Abstract

The difference in distance between the crowd and the camera results in different scales of the population in the image. In the study of crowd counting, this paper first proposed the method of adding deconvolution to multi-column network for the problem that the population size in the image does not affect the accuracy of counting, namely multi-column deconvolution neural network (Mutil-Column Deconvolutinal Neural Network, MCDNN). Compared with the results of the Multi-Column Convolutional Neural Network (MCNN) on the ShanghaiTech dataset, the MSE is reduced by 42.8 and the MAE is reduced by 26.6. On the UCF_CC_50 dataset, the MSE is reduced by 109.4 and the MAE is reduced. 86.3. Compared with other advanced methods, the proposed MCDNN network achieves better performance on the data set, effectively solves the problem of inaccurate population counting accuracy due to scale changes, and can generate a better quality density map.