Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465 Porto, Portugal.
Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465 Porto, Portugal.
Sensors (Basel). 2022 Jul 15;22(14):5286. doi: 10.3390/s22145286.
The crowd counting task has become a pillar for crowd control as it provides information concerning the number of people in a scene. It is helpful in many scenarios such as video surveillance, public safety, and future event planning. To solve such tasks, researchers have proposed different solutions. In the beginning, researchers went with more traditional solutions, while recently the focus is on deep learning methods and, more specifically, on Convolutional Neural Networks (CNNs), because of their efficiency. This review explores these methods by focusing on their key differences, advantages, and disadvantages. We have systematically analyzed algorithms and works based on the different models suggested and the problems they are trying to solve. The main focus is on the shift made in the history of crowd counting methods, moving from the heuristic models to CNN models by identifying each category and discussing its different methods and architectures. After a deep study of the literature on crowd counting, the survey partitions current datasets into sparse and crowded ones. It discusses the reviewed methods by comparing their results on the different datasets. The findings suggest that the heuristic models could be even more effective than the CNN models in sparse scenarios.
人群计数任务已成为人群控制的支柱，因为它提供了有关场景中人数的信息。它在视频监控、公共安全和未来活动规划等许多场景中都很有帮助。为了解决这些任务，研究人员提出了不同的解决方案。最初，研究人员采用了更传统的解决方案，而最近，由于其效率，人们的关注焦点集中在深度学习方法上，更具体地说是卷积神经网络（CNN）上。本综述通过关注它们的关键差异、优点和缺点来探讨这些方法。我们已经根据所提出的不同模型和它们试图解决的问题，对算法和作品进行了系统分析。主要重点是人群计数方法历史上的转变，从启发式模型到 CNN 模型的转变，通过识别每个类别并讨论其不同的方法和架构来实现。在对人群计数文献进行深入研究后，该调查将当前数据集分为稀疏数据集和拥挤数据集。它通过比较不同数据集上的结果来讨论所回顾的方法。研究结果表明，在稀疏场景中，启发式模型甚至可能比 CNN 模型更有效。
