一种基于卷积神经网络的轨道交通场景人群计数模型

现有的人群计数方法不能够完全适用于轨道交通场景中，为此，提出一种基于卷积神经网络的人群计数模型。模型采用VGG16作为前端网络提取浅层特征，提出一种基于Inception结构改进的M-Inception结构，结合空洞卷积构成后端网络，增大感受野，适应多监控角度下不同尺寸的行人目标；并提出一种融合行人总数估计损失和密度图损失的加权损失函数。将本文模型与4种现有模型进行对比实验，结果表明，提出的人群计数算法在地铁场景中的平均绝对误差和均方误差仅为1.46和2.13，优于4种对比模型。考虑到模型的实际应用，将模型部署到海思嵌入式芯片上，实测结果表明，模型可在嵌入式芯片上取得较高的计算速度和准确率，满足实际应用场景的需求。

A crowd counting model for rail transit scene based on convolutional neural network

The existing crowd counting methods are not suitable for the subway scene. Therefore, a crowd counting model based on convolutional neural network is proposed. The model takes the VGG16 as the front-end network to extract the shallow features, and an M-Inception structure is combined with the dilated convolution to form the back-end network, which can increase the receptive field and adapt to different sizes of pedestrian targets at different monitoring angles. And a weighted loss function combining the head count loss and density map loss is proposed. The proposed algorithm is compared with four existing models. The experimental results show that the Mean Absolute Error(MAE) and Mean Square Error(MSE) of the proposed algorithm are 1.46 and 2.13, better than those of the four comparison models. The proposed model is deployed to Hisilicon embedded chip. The test results show that the proposed model can achieve high computing speed and accuracy on the embedded chip, which can meet the requirements of the actual application scenarios.