Encoding-Decoding Multi-Scale Convolutional Neural Network for Crowd Counting

Aiming at the problems of multi-scale feature information loss, poor fusion and low quality of density map in the crowd counting method based on multi-column convolutional neural network, a new crowd counting method is proposed based on encoding-decoding multi-scale convolutional neural network. The encoder part adopts multi-column convolution to capture multi-scale features, expands the receptive field and reduces the amount of calculation via atrous space pyramid pooling, and retains the multi-scale feature and the context information of the image. The decoder part upsamples the encoder output to achieve effective fusion of the features with rich high-level semantic information and the features with rich low-level detail information to improve the output quality of the density map. To enhance the sensitivity of the network to counting, a new loss function is proposed by considering the previous pixel space loss and the counting error. Contrast experiments with previous methods on Shanghai Tech, Mall, and self-built datasets are conducted, and it is found that the mean absolute error and mean square error of this method on part_A of Shanghai Tech dataset are 8.3% and 21.3% lower than the previous optimal method, and 12.9% and 12.0% lower in part_B of Shanghai Tech dataset. The mean absolute error and mean square error decrease by 15.1% and 23.8% for the Mall dataset, and decrease by 13.5% and 7.1% for the self-built dataset. The experimental results on Shanghai Tech, Mall and self-built datasets show the higher accuracy and better robustness of the proposed method than the traditional methods. © 2020, Editorial Office of Journal of Xi'an Jiaotong University. All right reserved.