SA-InterNet: Scale-Aware Interaction Network for Joint Crowd Counting and Localization

Crowd counting and crowd localization are essential and challenging tasks due to uneven distribution and scale variation. Recent studies have shown that crowd counting and localization can complement and guide each other from two different perspectives of crowd distribution. How to learn the complementary information is still a challenging problem. To this end, we propose a Scale-aware Interaction Network (SA-InterNet) for joint crowd counting and localization. We design a dual-branch network to regress the density map and the localization map, respectively. The dual-branch network is mainly constructed with scale-aware feature extractors, which can obtain multi-scale features. To achieve mutual guidance and assistance of the two tasks, we design a density-localization interaction module by learning the complementary information. Our SA-InterNet can obtain accurate density map and localization map of an input image. We conduct extensive experiments on three challenging crowd counting datasets, including ShanghaiTech Part_A, ShanghaiTech Part_B and UCF-QNRF. Our SA-InterNet achieves superior performance to state-of-the-art methods.