Traffic Data Processing And Software Design Based On Feature Extraction

In recent years,the significant increase in per capita car ownership has brought about problems such as heavy traffic pressure,frequent traffic accidents,and increasingly poor road traffic environment.To this end,a real-time vehicle detection technology based on video analysis is gradually applied to road traffic monitoring.However,the existing traffic monitoring system only performs vehicle detection for a single lane,and cannot meet the demand for unified real-time monitoring of urban trunk roads.Therefore,this paper proposes an efficient real-time HOG-based vehicle detection algorithm,and performs ROI region division for the main road.At the same time,the vehicle trajectory data processing model is designed,which realizes a set of real-time detection of vehicles and calculation of traffic flow parameters.A vehicle detection system that determines road events.The specific research contents and results of this paper are as follows:(1)This paper studies the vehicle detection system under complex road background,and proposes a target detection algorithm based on HOG feature,with an accuracy rate of 89.12%.The calculation method of the integral map is used to improve the calculation rate of the HOG feature.In order to meet the requirements of high-concurrency real-time detection,an image preprocessing method based on pattern matching is proposed.This method can automatically determine the ROI area and eliminate the road background image,which effectively improves the processing speed of the detection algorithm.The improved target detection algorithm has an accuracy of 92.87%and an average detection speed of 22.62 ms/frame.(2)This paper analyzes and studies the traffic data processing methods in vehicle detection systems.An improved virtual coil method is proposed for the variable application scenario.The accuracy of the misdetected track point through the coil filter is increased to 94.62%.At the same time,the traffic data processing method based on effective trajectory is proposed,which realizes the calculation of traffic volume,speed,density,headway distance,headway distance,flow rate and space occupancy rate.Finally,based on the measured traffic flow parameters,real-time event detection and type discrimination are realized.The event detection rate of this method reached 94.1 7%.(3)The paper builds a vehicle detection system based on the C/S architecture.The algorithm studied in this paper is encapsulated into a dynamic link library for system call.It realizes the functions of real-time detection of vehicle driving information,road traffic conditions,detection of event occurrence,summary of traffic data and query history information.According to the on-site test and operation status of Xuzhou Highway,the vehicle detection system has high detection accuracy,strong overall performance and stable system operation,which can meet the needs of traffic management departments and be widely used.Through the method proposed in this paper,the target vehicle in complex background can be accurately detected,and the calculation of traffic flow parameters and the real-time discrimination of traffic events are realized.Effectively improve the emergency response capability and road management efficiency of the transportation department.