基于深度学习的人群计数算法综述

人群计数在视频监控、公共安全、智能商业等许多领域都有广泛的应用，近年来，随着深度学习的不断发展，人群计数已经成为计算机视觉领域研究的热点之一。本文根据提取特征方式的不同，将人群计数分为两类一类是传统方法，另一类是基于深度学习的方法，对基于卷积神经网络的方法进行重点分析和介绍；进一步介绍了人群计数领域的基准数据集和其他代表性数据集，实验结果表明，在人群密集和尺度变化较大的场景，基于卷积神经网络的方法优于传统方法，在尺度变化较大、人群较复杂的场景中多列网络比单列网络计数更加准确，效果更好；最后讨论了算法的未来发展方向。

Review of crowd counting algorithms based on deep learning

Crowd counting is widely used in video surveillance, public security, intelligent business and many other fields. In recent years, with the continuous development of deep learning, crowd counting has become one of the hot topics in the field of computer vision. In this paper, according to the different feature extraction methods, crowd counting is divided into two categories: one is traditional method, the other is based on deep learning method, and the method based on convolutional neural network is analyzed and introduced. Further introduces the population count in the field of benchmark data sets and other representative data sets, the experimental results show that the larger changes in the crowded and scale, based on the convolution of the neural network method is superior to the traditional method, the scale change is bigger, more complex scenarios crowd more columns than a single network count more accurate, more effective; Finally, the future development direction of the algorithm is discussed.