Design And Implementation Of Intelligent Monitoring And Identification System For Passenger Flow In Communication Stores

As a physical marketing channel,communication stores play an irreplaceable role in corporate brand image building,product business promotion,and customer service experience improvement.The communication store operation management system is committed to achieving multi-level and all-round organization management and information services.Store customer flow monitoring and identification is one of its important links.The traditional store's passenger flow monitoring and identification mainly rely on manual counting,and the work efficiency and accuracy are difficult to meet the actual development needs.3.This study takes "intelligent passenger flow monitoring" as the core and constructs a set of intelligent monitoring and identification system for passenger flow of communication stores,aiming to improve this situation.Analysis of development needs shows that the intelligent monitoring and identification system for passenger flow of communication stores should include content such as organizational management,performance evaluation,and on-site management.Based on this,this study designs three main functional modules of organizational management,operation management,and passenger flow monitoring.This research uses C / S framework,Visual Studio,C # language,ASP.NET and other development technologies to complete the system design work,and builds a suitable operating environment to demonstrate the system function operation.The system server includes an image acquisition module,a user management module,a log management module,and a video analysis module;the client includes a video preview module,a PTZ control module,and a video analysis module.In the system function design,passenger flow identification is the core module.The target detection method based on deep learning is widely used.The multi-layer representation describes the target features in depth,and the target feature detection accuracy is high;but there are certain limitations,the target motion information is not considered,and the detection results are prone to the target misdetection or false detection aims.This research combines an object modeling method and a deep neural network to design an improved passenger flow recognition algorithm,making full use of the appearance characteristics and motion information of the object to obtain higher accuracy detection results.The experimental results of the algorithm show that the algorithm is effective and can meet the needs of passenger flow recognition in complex environments.The intelligent monitoring and identification system for the passenger flow of communication stores realizes the effect display,and the system functions meet the expected goals.