Abstract:

In view of the existing monitoring system’s inability to cope with the problems of cross-infection and traceability difficulties in the epidemic environment， a design scheme for a public transportation detection system based on edge computing was proposed. Firstly， a graph database was established to store passengers and ride information， and at the same time a dual database model was used to prevent the blockage caused by building index， thereby achieving the balance between insertion efficiency and search efficiency. Then， in the extraction of vehicle and human image information， the HSV （Hue Saturation Value） color space was used to preprocess the image， and a three-dimensional space model of face was established to improve the recognition accuracy of the neural network. When the object wore a mask， the feature point information was able to be regressed through the obvious nose tip feature points， lower jaw feature points， and unobstructed nose bridge feature points. Finally， k-hop search was used to find close contacts quickly. In the feature comparison test， the correct rates of this model are 99.44% and 99.23% on BioID dataset and PubFig dataset， respectively， and the false negative rates of the model on the two datasets are both less than 0.01%. In the graph search efficiency test， there is no big difference between the graph database and the relational database when searching at a shallow level. When the search level becomes deeper， the graph database is more efficient. After verifying the theoretical feasibility， the actual environment of buses and bus stops was simulated. In the test， the proposed system has the recognition accuracy of 99.98%， and the average recognition time of about 21 ms， which meets the requirements of epidemic monitoring. The proposed system design can meet the special needs of public safety during the epidemic period， and can realize the functions of person recognition， route recording， and potential contact search， which can effectively ensure public transportation safety.

Key words: edge computing, public transportation, facial feature, graph database, neural network, HSV (Hue Saturation Value) color space, epidemic monitoring

摘要：

现有监测系统无法很好地应对疫情环境下存在的交叉传染以及追溯困难等问题，因此提出了一套基于边缘计算的公共交通检测系统的设计方案。首先，建立图数据库来储存乘车人员与乘车信息，同时使用双数据库模型防止建立索引带来的阻塞，从而完成插入效率与搜索效率的均衡；其次，在车辆人像信息提取中，采用HSV色彩空间对图片进行预处理，并建立人脸三维空间模型来提升神经网络的识别准确率，在目标佩戴口罩时，通过较明显的鼻尖特征点、下颌特征点与未遮挡的鼻梁部特征点回归出其口鼻等特征点信息；最后，通过k度搜索快速找出密切接触乘客。在特征对比测试中，该方案在BioID数据集和PubFig数据集上分别达到了99.44%和99.23%的正确率，且在两数据集上的假阴性率均小于0.01%；在图搜索效率测试中，在浅层次搜索的时候，图数据库与关系型数据库并无较大差异，当搜索层次变深时，图数据库效率更高；在验证理论可行性之后，模拟了公交车与公交站的实际环境，经测试所提系统在其中的识别准确率为99.98%，识别时间平均约为21 ms，符合疫情监测的要求。所提系统设计可以满足疫情时期公共安全的特殊需求，能够实现人员甄别、路径记录、潜在接触者搜索等功能，从而有效地保证公共交通安全。

关键词: 边缘计算, 公共交通, 面部特征, 图数据库, 神经网络, HSV色彩空间, 疫情监测