Single-column CNN for crowd counting with pixel-wise attention mechanism

• Published in: Neural computing & applications Vol. 32; no. 7; pp. 2897 - 2908
• Main Authors: Wang, Bisheng, Cao, Guo, Shang, Yanfeng, Zhou, Licun, Zhang, Youqiang, Li, Xuesong
• Format: Journal Article
• Language: English
• Published: London Springer London 01.04.2020; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Datasets; Density; Image classification; Image Processing and Computer Vision; Image quality; Modules; Original Article; Pixels; Probability and Statistics in Computer Science; FCN; CNN; Pixel-wise attention mechanism; Crowd counting
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-018-3810-9

This paper presents a novel method for accurate people counting in highly dense crowd images. The proposed method consists of three modules: extracting foreground regions (EF), pixel-wise attention mechanism (PAM) and single-column density map estimator (S-DME). EF can suppress the disturbance of complex background efficiently with a fully convolutional network, PAM performs pixel-wise classification of crowd images to generate high-quality local crowd density maps, and S-DME is a carefully designed single-column network that can learn more representative features with much fewer parameters. In addition, two new evaluation metrics are introduced to get a comprehensive understanding of the performance of different modules in our algorithm. Experiments demonstrate that our approach can get the state-of-the-art results on several challenging datasets including our dataset with highly cluttered environments and various camera perspectives.