Research On Night Vehicle Detection And Tracking Based On Video

With the development of computer image processing technology,video based vehicle detection technology plays an important role in the research of intelligent traffic management system.Daytime vehicle detection technology is already quite perfect,and has been widely used,but the nighttime video has low quality for a long time,so video based vehicle detection technology in the night has a lot of constraints.In recent years,with the rapid development of computer image processing technology and monitoring devices,traffic videos are becoming more and more clear,and night video quality has also been greatly improved.Due to the complexity of the night scene,the reference substance is too few and the light is too weak,so the vehicle detection algorithm that is suitable for daytime cannot be applied to nighttime.According to the above-mentioned problems this paper researched on the motion information of vehicles and local feature information of vehicle at night.The main research results are as follows :(1)An improved algorithm for the extraction of the vehicle headlights was designed.There will be a lot of noise when we directly extract the light on video,so before the lights are extracted,the median filtering is done to smooth the image,so that filtering out the noise in the video.Then we can get a priori knowledge of the brightness of the headlights based on statistical knowledge,then using one dimensional maximum entropy threshold method to obtain the adaptive threshold.The threshold is used to segment the image,and then extracting the bright block as a candidate lamp,finally filtering out the unstable bright blocks to get the car lights.(2)A light grouping method based on spatial characteristics is proposed.Consider that the lights’ characteristics of the same car are very similar,such as the area,aspect ratio,the location of the center of mass and so on.This paper combines the spatial characteristics of the lights and vehicle characteristics to define rules for lights matching.This method not only has a very good matching effect for a car with more than two car lights,but also has a good effect on the same direction of the vehicle with the same speed.(3)A vehicle tracking algorithm based on Kalman filter is realized.Because the change of the vehicle is stable between the adjacent frames in the night high speed video,in this paper,we use the information of the center of mass and the surrounding of the vehicle to match the vehicle.Kalman filter is used in the design of the model,and according to the characteristic information of the vehicle lamp in the curre nt frame,the possible position of the vehicle in the next frame is predicted.Using the detected vehicle information to update the model,and then repeat ing the process to complete the vehicle tracking(4)An algorithm based on BOF is proposed that identifies the type of high speed vehicle at night is proposed.This paper presents an improved algorithm for vehicle classification based on visual word bag.At first using SURF to describe the characteristics of the vehicle at night;then the feature points are clustered by the improved K-means clustering algorithm based on the max min distance algorithm to form a visual dictionary,finally using SVM to complete the classification.The experimental results show that the method can identify the vehicle at night with high accuracy,and the processing speed is very fast.