Multi-Level Attentive Convoluntional Neural Network for Crowd Counting

• URL: http://arxiv.org/abs/2105.11422v1
• Date: Mon, 24 May 2021 17:29:00 GMT
• Title: Multi-Level Attentive Convoluntional Neural Network for Crowd Counting
• Authors: Mengxiao Tian, Hao Guo, Chengjiang Long
• Abstract summary: We propose a multi-level attentive Convolutional Neural Network (MLAttnCNN) for crowd counting. We extract high-level contextual information with multiple different scales applied in pooling. We use multi-level attention modules to enrich the characteristics at different layers to achieve more efficient multi-scale feature fusion.
• Score: 12.61997540961144
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently the crowd counting has received more and more attention. Especially the technology of high-density environment has become an important research content, and the relevant methods for the existence of extremely dense crowd are not optimal. In this paper, we propose a multi-level attentive Convolutional Neural Network (MLAttnCNN) for crowd counting. We extract high-level contextual information with multiple different scales applied in pooling, and use multi-level attention modules to enrich the characteristics at different layers to achieve more efficient multi-scale feature fusion, which is able to be used to generate a more accurate density map with dilated convolutions and a $1\times 1$ convolution. The extensive experiments on three available public datasets show that our proposed network achieves outperformance to the state-of-the-art approaches.

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