Multi-branch Segmentation-guided Attention Network for crowd counting

Abstract

Crowd counting has gained increasing attention recently owing to its importance in public safety. However, it remains a challenging task due to background complexities and high annotation costs. To address these challenges, we propose the Multi-branch Segmentation-guided Attention Network (MSGANet). To deal with the complex background, we incorporate segmentation-guided attention branches into both shallow and deep layers of the baseline model, allowing simultaneous consideration of spatial and semantic information. Multi-level attention maps enable the network to minimize the influence of complex backgrounds while focusing on the regions containing crowds. To tackle the issue of costly annotations, MSGANet utilizes only point annotations to generate ground truth density and segmentation maps, eliminating additional expenses. Our results demonstrate that our approach achieves highly competitive performance compared to state-of-the-art crowd counting methods.