| Paper ID | IVMSP-17.6 |
| Paper Title |
SANET++: ENHANCED SCALE AGGREGATION WITH DENSELY CONNECTED FEATURE FUSION FOR CROWD COUNTING |
| Authors |
Siyang Pan, Yanyun Zhao, Fei Su, Zhicheng Zhao, Beijing University of Posts and Telecommunications, China |
| Session | IVMSP-17: Looking at People |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 16:30 - 17:15 |
| Presentation Time: | Wednesday, 09 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Image, Video, and Multidimensional Signal Processing: [IVARS] Image & Video Analysis, Synthesis, and Retrieval |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
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. |
