论文标题
一项有关基于深度学习的单图像人群计数的调查：网络设计，损失功能和监督信号
A Survey on Deep Learning-based Single Image Crowd Counting: Network Design, Loss Function and Supervisory Signal
论文作者
论文摘要
单图像人群计数是一个具有挑战性的计算机视觉问题，在公共安全，城市规划，交通管理等方面的广泛应用。随着深度学习技术的最新发展，近年来，人群的计数引起了很多关注并取得了巨大的成功。这项调查是为了通过系统地审查和总结该领域的200多件作品来提供有关基于深度学习的人群计数技术的最新进展的全面摘要。我们的目标是对最近的方法进行最新的审查，并在该领域的新研究人员进行最新的研究原理和权衡。在介绍了公开可用的数据集和评估指标之后，我们通过对三个主要的设计模块进行了详细比较来回顾最近的进步：深层神经网络设计，损失功能和监督信号。我们使用公共数据集和评估指标研究和比较方法。我们以一些未来的方向结束了调查。
Single image crowd counting is a challenging computer vision problem with wide applications in public safety, city planning, traffic management, etc. With the recent development of deep learning techniques, crowd counting has aroused much attention and achieved great success in recent years. This survey is to provide a comprehensive summary of recent advances on deep learning-based crowd counting techniques via density map estimation by systematically reviewing and summarizing more than 200 works in the area since 2015. Our goals are to provide an up-to-date review of recent approaches, and educate new researchers in this field the design principles and trade-offs. After presenting publicly available datasets and evaluation metrics, we review the recent advances with detailed comparisons on three major design modules for crowd counting: deep neural network designs, loss functions, and supervisory signals. We study and compare the approaches using the public datasets and evaluation metrics. We conclude the survey with some future directions.
