关键词: 人群计数, 卷积神经网络, 多任务学习, 人群密度估计, 人群密度等级分类

Abstract: In an attempt to improve the counting accuracy of crowd images, a multi-task multi-level convolutional neural network is designed in this paper. Multi-level neural network is constructed with combinations of convolution and upsampling. The network has the advantages of combining the detailed information extracted by the shallow layer and the high-level semantic information extracted by the deep layer. On this basis, multi-task learning is used to improve network performance. Multi-task learning is divided into two parts, namely, crowd density estimation and crowd density classification. The high-resolution layer of the network is connected to crowd density estimation task, and the deep layer is connected to crowd density level classification task. A new loss function is defined by merging the losses of two tasks. The experiment is carried out on the crowd public data set ShanghaiTech, WorldExpo’10 and UCF_CC_50. The experimental results reveal that the proposed network has better accuracy and robustness in crowd counting.

Key words: crowd counting, Convolution Neural Network（CNN）, multi-task learning, crowd density estimation, crowd density classification