Crowd counting network based on attention feature fusion and multi-column feature enhancement

Density map estimation is commonly used for crowd counting. However, using it alone may make some individuals difficult to recognize, due to the problems of target occlusions, scale variations, complex background and heterogeneous distribution. To alleviate these problems, we propose a two-stage crowd counting network based on attention feature fusion and multi-column feature enhancement (AFF-MFE-TNet). In the first stage, AFF-MFE-TNet transforms the input image into a probability map. In the second stage, a multi-column feature enhancement module is constructed to enhance features by expanding the receptive fields, a dual attention feature fusion module is designed to adaptively fuse the features of different scales through attention mechanisms, and a triple counting loss is presented for AFF-MFE-TNet, which can fit the ground truth probability maps and density maps better, and improve the counting performance. Experimental results show that AFF-MFE-TNet can effectively improve the accuracy of crowd counting, as compared with the state-of-the-art.