Crowd counting network based on attention feature fusion and multi-column feature enhancement

IF 2.6 4区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of Visual Communication and Image Representation Pub Date : 2024-10-28 DOI:10.1016/j.jvcir.2024.104323

Qian Liu, Yixiong Zhong, Jiongtao Fang

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引用次数: 0

Abstract

Density map estimation is commonly used for crowd counting. However, using it alone may make some individuals difficult to recognize, due to the problems of target occlusions, scale variations, complex background and heterogeneous distribution. To alleviate these problems, we propose a two-stage crowd counting network based on attention feature fusion and multi-column feature enhancement (AFF-MFE-TNet). In the first stage, AFF-MFE-TNet transforms the input image into a probability map. In the second stage, a multi-column feature enhancement module is constructed to enhance features by expanding the receptive fields, a dual attention feature fusion module is designed to adaptively fuse the features of different scales through attention mechanisms, and a triple counting loss is presented for AFF-MFE-TNet, which can fit the ground truth probability maps and density maps better, and improve the counting performance. Experimental results show that AFF-MFE-TNet can effectively improve the accuracy of crowd counting, as compared with the state-of-the-art.

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基于注意力特征融合和多列特征增强的人群计数网络

密度图估算通常用于人群计数。然而，由于目标遮挡、尺度变化、复杂背景和异质分布等问题，仅使用密度图估计可能会使一些个体难以识别。为了缓解这些问题，我们提出了一种基于注意力特征融合和多列特征增强的两阶段人群计数网络（AFF-MFE-TNet）。在第一阶段，AFF-MFE-TNet 将输入图像转换为概率图。在第二阶段，构建了多列特征增强模块，通过扩大感受野来增强特征；设计了双注意特征融合模块，通过注意机制自适应地融合不同尺度的特征；提出了 AFF-MFE-TNet 的三重计数损失，它能更好地拟合地面实况概率图和密度图，提高计数性能。实验结果表明，与最先进的技术相比，AFF-MFE-TNet 可以有效提高人群计数的准确性。

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来源期刊

Journal of Visual Communication and Image Representation 工程技术-计算机：软件工程

CiteScore

5.40

自引率

11.50%

发文量

188

审稿时长

9.9 months

期刊介绍： The Journal of Visual Communication and Image Representation publishes papers on state-of-the-art visual communication and image representation, with emphasis on novel technologies and theoretical work in this multidisciplinary area of pure and applied research. The field of visual communication and image representation is considered in its broadest sense and covers both digital and analog aspects as well as processing and communication in biological visual systems.