SANet++: Enhanced Scale Aggregation with Densely Connected Feature Fusion for Crowd Counting
Siyang Pan, Yanyun Zhao, Fei Su, Zhicheng Zhao · 2021
Crowd counting has gained considerable attention recently but remains challenging mainly due to large scale variations. In this paper, we present SANet++ with a novel architecture to generate high-quality density maps and further perform accurate counting. SANet++ obtains enhanced multi-scale representation with densely connected feature fusion between branches. Our approach avoids information redundancy while exploits complementary features at different scales. In addition, we introduce a novel Bulk loss which incorporates the spatial correlation within a whole patch. This global structural supervision enforces the network to learn the interactions between pixels without limitations on region size. Our SANet++ outperforms state-of-the-art crowd counting approaches according to extensive experiments conducted on three major datasets.