Railway Station Pedestrian Flow Detection And LabVIEW Implementation Based On Deep Learning

Due to the rapid development of science and technology,the pace of people’s life is accelerating,and the number of people traveling every year is also increasing.As an important passenger infrastructure in the city,the railway station is the distribution center for a large number of passengers.Due to the complex building structure and large passenger flow of large railway stations,if personnel are specially assigned to count the flow of people,it will consume a lot of manpower,and counting is more difficult.The advancement of science and technology has made digital technology show its advantages in practical applications,allowing us to count the flow of people more quickly and accurately.In this paper,through the combination of deep learning model and Lab VIEW software,machine vision algorithm is used to complete the statistics of train station traffic,which provides a reference for the daily management of various departments,and can save a lot of manpower.This kind of intelligent technology also provides help for urban management and has strong practicality.The following main contents are included in this article:(1)First,the significance and development status of pedestrian flow statistics are expounded,the key technologies required in the research of pedestrian detection algorithms are introduced,and the overall flow of pedestrian flow statistics is given.(2)Secondly,on the basis of understanding the traditional pedestrian detection algorithm and learning the pedestrian detection algorithm based on the deep learning network,the two deep learning networks,TensorFlow and YOLOv5,are determined as the pedestrians in the pedestrian traffic statistics algorithm system in this paper.detection algorithm.(3)Finally,after determining the pedestrian detection algorithm based on deep learning,the tracking algorithm,association algorithm and trajectory analysis algorithm are further studied in this paper,so that the structure of the entire pedestrian traffic statistics algorithm is basically complete.Due to the epidemic situation and station restrictions,it is impossible to collect videos at the railway station frequently.All the sample videos collected in the campus are analyzed to simulate the crowd flow field of the railway station,and the results of the algorithm processing are analyzed.The conclusion is that the deep learning-based pedestrian flow statistics algorithm adopted can be applied in practice and it has high practicability.