Crowd counting using a self-attention multi-scale cascaded network

Recent developments of crowd analysis and behaviour prediction have attracted much attention. Crowd counting, as the essential and challenging task in crowd analysis, is riddled with many issues, such as large scale variations, serious occlusion, and so on. In this study, a self-attention-based multi-scale cascaded network called SAMC-Net to estimate density map for crowd counting, especially for high congested scene, is proposed. The proposed SAMC-Net consists of two components: a classification sub-network for density estimation and an end-to-end multi-scale convolution neural network for crowd counting. In order to reduce the negative effect of multi-scale issue on crowd counting task, the main network is designed as a multi-scale structure similar to U-Net. In order to enhance the crowd feature representation, this study proposes a self-attention-based crowd feature extraction way and uses it in the proposed SAMC-Net. Extensive experiments demonstrate the feasibility, effectiveness and robustness of the proposed SAMC-Net.