Relational Attention Network for Crowd Counting

• Published in: Proceedings / IEEE International Conference on Computer Vision pp. 6787 - 6796
• Main Authors: Zhang, Anran, Shen, Jiayi, Xiao, Zehao, Zhu, Fan, Zhen, Xiantong, Cao, Xianbin, Shao, Ling
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2019
• Subjects: Computational modeling; Correlation; Estimation; Feature extraction; Fuses; Image reconstruction; Task analysis
• ISSN: 2380-7504
• DOI: 10.1109/ICCV.2019.00689

Crowd counting is receiving rapidly growing research interests due to its potential application value in numerous real-world scenarios. However, due to various challenges such as occlusion, insufficient resolution and dynamic backgrounds, crowd counting remains an unsolved problem in computer vision. Density estimation is a popular strategy for crowd counting, where conventional density estimation methods perform pixel-wise regression without explicitly accounting the interdependence of pixels. As a result, independent pixel-wise predictions can be noisy and inconsistent. In order to address such an issue, we propose a Relational Attention Network (RANet) with a self-attention mechanism for capturing interdependence of pixels. The RANet enhances the self-attention mechanism by accounting both short-range and long-range interdependence of pixels, where we respectively denote these implementations as local self-attention (LSA) and global self-attention (GSA). We further introduce a relation module to fuse LSA and GSA to achieve more informative aggregated feature representations. We conduct extensive experiments on four public datasets, including ShanghaiTech A, ShanghaiTech B, UCF-CC-50 and UCF-QNRF. Experimental results on all datasets suggest RANet consistently reduces estimation errors and surpasses the state-of-the-art approaches by large margins.