Beijing Key Lab of Urban Intelligent Traffic Control Technology, North China University of Technology, Beijing 100144, China.
Key Laboratory of Operation Safety Technology on Transport Vehicles, Research Institute of Highway, Ministry of Transport, Beijing 100088, China.
Sensors (Basel). 2022 Jun 25;22(13):4807. doi: 10.3390/s22134807.
At present, the COVID-19 pandemic still presents with outbreaks occasionally, and pedestrians in public areas are at risk of being infected by the viruses. In order to reduce the risk of cross-infection, an advanced pedestrian state sensing method for automated patrol vehicles based on multi-sensor fusion is proposed to sense pedestrian state. Firstly, the pedestrian data output by the Euclidean clustering algorithm and the YOLO V4 network are obtained, and a decision-level fusion method is adopted to improve the accuracy of pedestrian detection. Then, combined with the pedestrian detection results, we calculate the crowd density distribution based on multi-layer fusion and estimate the crowd density in the scenario according to the density distribution. In addition, once the crowd aggregates, the body temperature of the aggregated crowd is detected by a thermal infrared camera. Finally, based on the proposed method, an experiment with an automated patrol vehicle is designed to verify the accuracy and feasibility. The experimental results have shown that the mean accuracy of pedestrian detection is increased by 17.1% compared with using a single sensor. The area of crowd aggregation is divided, and the mean error of the crowd density estimation is 3.74%. The maximum error between the body temperature detection results and thermometer measurement results is less than 0.8°, and the abnormal temperature targets can be determined in the scenario, which can provide an efficient advanced pedestrian state sensing technique for the prevention and control area of an epidemic.
目前，新冠疫情仍时有爆发，公共场所的行人存在感染病毒的风险。为降低交叉感染风险，提出一种基于多传感器融合的自动巡逻车行人状态感知先进方法，用于感知行人状态。首先，获取欧几里得聚类算法和 YOLO V4 网络输出的行人数据，采用决策级融合方法提高行人检测精度。然后，结合行人检测结果，基于多层融合计算人群密度分布，并根据密度分布估计场景中的人群密度。此外，一旦人群聚集，就会通过热红外摄像机检测聚集人群的体温。最后，基于所提出的方法，设计了一个自动巡逻车实验来验证准确性和可行性。实验结果表明，与使用单一传感器相比，行人检测的平均准确率提高了 17.1%。对人群聚集区域进行划分，人群密度估计的平均误差为 3.74%。体温检测结果与体温计测量结果之间的最大误差小于 0.8°，可以在场景中确定异常体温目标，为疫情防控区域提供高效的先进行人状态感知技术。
