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Learning Multi-Level Features to Improve Crowd Counting
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DOI：10.1109/ACCESS.2020.3039998.png)
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Abstract
En 中文
Crowd counting is a task that aims to estimate the number of people in an image. Recent crowd counting methods make significant progress by employing convolutional neural networks to regress crowd density maps. One of the most challenging problems in this task is the drastic scale variation of the region of interest in images. In this paper, a Feature Fusion Attention Network (FFANet) is proposed for crowd counting. Firstly, the VGG16 network is adapted as the backbone of the FFANet to extract the features of crowd images. Then, the extracted features are fused by the subsequent two stages. Specifically, the information enhancement operations on the multi-levels features are conducted by Feature Fusion Attention Module (FFAM), which are further refined by the Residual Block (RB). Finally, the features are processed by the Compression Module (CM) to generate a density map. To demonstrate the effectiveness, the proposed algorithm is verified on three benchmark datasets. Evaluation of the algorithm performances in comparison with other state-of-the-art methods indicates the proposed FFANet outperforms the existing methods.
Keywords:
Feature extraction
Task analysis
Semantics
Licenses
Data mining
Convolutional neural networks
Predictive models
Crowd counting
scale variation
feature fusion attention
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