In recent years, urgent needs for counting crowds and vehicles have greatly promoted research of crowd counting and density estimation. Benefiting from the rapid development of deep learning, the counting performance has been greatly improved, and the application scenarios have been further expanded. Aiming to deeply understand the development status of crowd counting and density estimation, we introduce and analyze the typical methods in this field and especially focus on elaborating deep learning-based counting methods. We summarize the existing approaches into four categories, i.e., detection-based, regression-based, convolutional neural network based and video-based. Each category is explicated in great detail. To provide more concrete reference, we compare the performance of typical methods on the popular benchmarks. We further elaborate on the datasets and metrics for the crowd counting community and discuss the work of solving the problem of small-sample-based counting, dataset annotation methods and so on. Finally, we summarize various challenges facing crowd counting and their corresponding solutions and propose a set of development trends in the future.

近年来，对人群和车辆计数的迫切需求极大地促进了人群计数和密度估计的研究。得益于深度学习的飞速发展，计数性能得到了极大的提高，应用场景得到了进一步扩展。为了深入了解人群计数和密度估计的发展状况，我们介绍和分析了该领域的典型方法，尤其着重阐述了基于深度学习的计数方法。我们将现有方法概括为四类，即基于检测，基于回归，基于卷积神经网络和基于视频。每个类别都有详尽的说明。为了提供更具体的参考，我们将比较常用基准上典型方法的性能。我们将进一步详细介绍人群计数社区的数据集和指标，并讨论解决基于小样本的计数，数据集注释方法等问题的工作。最后，我们总结了人群计数面临的各种挑战及其相应的解决方案，并提出了未来的发展趋势。