期刊
SENSORS
卷 21, 期 11, 页码 -出版社
MDPI
DOI: 10.3390/s21113777
关键词
crowd counting; crowd density estimation; multi-scale context learning; crowd localization; remote sensing object counting
资金
- Natural Science Foundation of Shanghai [19ZR1455300]
- National Natural Science Foundation of China [61806126]
The proposed MSCANet network leverages spatial context information for crowd density estimation, showing compelling performance in challenging crowd counting benchmarks and proving effective in tasks such as crowd localization and remote sensing object counting.
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 x 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.
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