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  • Title: Context-Aware Multi-Scale Aggregation Network for Congested Crowd Counting.
    Author: Huang L, Shen S, Zhu L, Shi Q, Zhang J.
    Journal: Sensors (Basel); 2022 Apr 22; 22(9):. PubMed ID: 35590922.
    Abstract:
    In this paper, we propose a context-aware multi-scale aggregation network named CMSNet for dense crowd counting, which effectively uses contextual information and multi-scale information to conduct crowd density estimation. To achieve this, a context-aware multi-scale aggregation module (CMSM) is designed. Specifically, CMSM consists of a multi-scale aggregation module (MSAM) and a context-aware module (CAM). The MSAM is used to obtain multi-scale crowd features. The CAM is used to enhance the extracted multi-scale crowd feature with more context information to efficiently recognize crowds. We conduct extensive experiments on three challenging datasets, i.e., ShanghaiTech, UCF_CC_50, and UCF-QNRF, and the results showed that our model yielded compelling performance against the other state-of-the-art methods, which demonstrate the effectiveness of our method for congested crowd counting.
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