Congested Crowd Counting via Adaptive Multi-Scale Context Learning

• Published in: Sensors (Basel, Switzerland) Switzerland), 2021-05, Vol.21 (11), p.3777
• Main Authors: Zhang, Yani, Zhao, Huailin, Duan, Zuodong, Huang, Liangjun, Deng, Jiahao, Zhang, Qing
• Format: Article
• Language: English
• Subjects: Convolution; crowd counting; crowd density estimation; crowd localization; Datasets; Experiments; Localization; Mathematical functions; Methods; multi-scale context learning; Network management systems; Performance evaluation; Remote sensing; remote sensing object counting
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21113777

In this paper, we propose a novel congested crowd counting network for crowd density estimation, i.e., the Adaptive Multi-scale Context Aggregation Network (MSCANet). MSCANet efficiently leverages the spatial context information to accomplish crowd density estimation in a complicated crowd scene. To achieve this, a multi-scale context learning block, called the Multi-scale Context Aggregation module (MSCA), is proposed to first extract different scale information and then adaptively aggregate it to capture the full scale of the crowd. Employing multiple MSCAs in a cascaded manner, the MSCANet can deeply utilize the spatial context information and modulate preliminary features into more distinguishing and scale-sensitive features, which are finally applied to a 1 × 1 convolution operation to obtain the crowd density results. Extensive experiments on three challenging crowd counting benchmarks showed that our model yielded compelling performance against the other state-of-the-art methods. To thoroughly prove the generality of MSCANet, we extend our method to two relevant tasks: crowd localization and remote sensing object counting. The extension experiment results also confirmed the effectiveness of MSCANet.