2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDIVMSP-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
SessionIVMSP-17: Looking at People
LocationGather.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
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.