Object counting method based on dual attention network

Abstract

The challenging problem that the authors solved in this study is to precisely estimate the number of objects in an image. Combining the spatial attention mechanism and pyramid structure, a novel atrous pyramid attention module is introduced to extract precise dense multi-scale features for object counting. Also, a global attention feature module is designed to enhance the ability of the network to learn feature representation based on channel attention mechanism. Combining the proposed atrous pyramid attention module and global attention feature module, a novel object counting method based on a dual attention network is established in this study. The experiments on public vehicle counting dataset including TRANCOS and crowd counting dataset including Mall and Shanghitech_A datasets demonstrate the proposed method achieves competitive performance, and the ablation study verifies the structure rationality of the designed modules.