During the operation of the station,it is of great significance to obtain real-time passenger flow information,monitor,warn and analyze the gathering behavior and key areas in the safety assurance and management of the station.At present,the number of monitoring hardware equipment in the station is increasing and the equipment is complex.It is diffi-cult to achieve real-time,all-round and multi angle passenger flow density and aggregation analysis of the station solely relying on the early warning function of the monitoring system.To solve this problem,this paper proposes a method based on the combination of YOLOv5+DeepSort and HRNet model,which realizes the detection of personnel speed,direction,density,anomaly aggregation,anomaly intrusion,barrier delivery and ticket evasion detection.We have carried out experi-ments on the real-time monitoring data set of the station and the relevant internet data set collected.The experimental results show that the algorithm proposed in this paper can achieve the detection task,and has the ability to analyze the monitoring video and practical applications in a real-time and efficient manner.On this basis,we have developed a set of back-end service and front-end management platform of the station video behavior analysis system,which is of great significance and develop-ment prospects for passenger travel security and safe operation of the station.
维普
万方数据