MSFANet: Crowd Density Estimation Based on Multi-scale Feature Adaptation

Abstract

To solve the problem that the number of heads cannot be accurately extracted due to scale changes in crowd counting, a crowd density estimation based on multi-scale feature adaptive network was proposed(MSFANet). Firstly, deep convolution neural network is used to extract semantic features of different scales. Then, the expansion convolution network is introduced, and the expansion convolution in the scale-up unit is combined with the traditional convolution to further increase the receptive field and reduce the information loss caused by channel competition. Finally, channel attention is introduced to enhance the ability of multi-scale feature extraction and ensure the integrity of relevant and important information in the image. Experiments on datasets (ShanghaiTech, UCF_CC_50 and WorldExpo10) show that this algorithm is more accurate and robust than the current mainstream crowd counting algorithms.