RA-MISNet: multilevel information sharing network based on residual attention for crowd counting

Jun Sang; Shaoli Tian; Jinghan Tan; Mohammad S. Alam; Yan Chen

doi:10.1117/12.2618271

27 May 2022 RA-MISNet: multilevel information sharing network based on residual attention for crowd counting

Jun Sang, Shaoli Tian, Jinghan Tan, Mohammad S. Alam, Yan Chen

Proceedings Volume 12101, Pattern Recognition and Tracking XXXIII; 121010K (2022) https://doi.org/10.1117/12.2618271
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Crowd counting has been a popular research topic in the field of computer vision due to the variation of human head scales and the interference of background noise. Some existing methods use multi-level feature fusion to solve scale variation, but the problem of background noise interference may be more serious due to the involvement of shallow features in the feature fusion process. In this paper, we propose a Multilevel Information Sharing Network based on Residual Attention(RA-MISNet) to solve this problem. The RA-MISNet consists of a feature extraction component, an information sharing module and a residual attention density map estimator. On the basis of solving the multi-scale problem, the residual attention mechanism is adopted by our proposed method to refine the population distribution information in sharing features at all levels, which can reduce the interference of complex texture background on density map regression. Furthermore, owing to the severe label noise interference problem in high-density crowd areas, we design a Regional Multi-level Segmentation Loss (RMS Loss) to divide the multi-level density regions with different label noise rates in a single crowd image and apply the corresponding granularity supervision constraints for each density level region. Extensive experiments on three crowd counting datasets (ShanghaiTech, UCF CC 50, UCF-QNRF) demonstrate the effectiveness and superiority of the proposed methods.

Citation Download Citation

Jun Sang, Shaoli Tian, Jinghan Tan, Mohammad S. Alam, and Yan Chen "RA-MISNet: multilevel information sharing network based on residual attention for crowd counting", Proc. SPIE 12101, Pattern Recognition and Tracking XXXIII, 121010K (27 May 2022); https://doi.org/10.1117/12.2618271

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KEYWORDS

Image segmentation

Feature extraction

Head

Data modeling

Network architectures

Convolution

Computer vision technology

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