关键词: 人群计数, 卷积神经网络, 深度学习, 密度图

Abstract:

In the field of public security, image-based crowd counting has important social significance and application prospects. The difficulty lies in crowd occlusion, uneven density distribution, background noise and large scale of human scale and appearance in the scene. A deep convolutional neural network structure is proposed. On the one hand, the network structure similar to VGG16 is used to learn the deep semantic information in the pictures, on the other hand, the multi-column neural network is used to learn the feature information of various head sizes. The feature maps obtained from the branch networks with different sizes of receptive fields and depths can be combined to effectively collect the underlying detail features and high-level semantic information in the images. The number of people is calculated by combining these two parts together. Tested on the ShanghaiTech dataset, the mean absolute errors of Part_A and Part_B are 72.0 and 10.1; the mean square errors of Part_A and Part_B are 107.9 and 16.0, respectively.

Key words: crowd counting, convolutional neural network, deep learning, density map