Research On Detection And Tracking Algorithm For High-speed Front-moving Vehicle

The times are advancing.With the ever-increasing economic level and the rapid development of science and technology,the quality of people’s lives is flourishing.The civilianization of automobiles is the most obvious manifestation of the rapid development of intelligent transportation.Safe driving,safe assisted driving techniques,etc.have always been a hot topic,and vehicle detection and vehicle tracking have once become the focus of attention.The existing technology about safe assisted driving has not been able to meet the needs of users for safety technology.Compared with the past,the current traffic scene becomes more complex and changeable,especially in the context of vehicles such as highways that must travel at high speeds,so whether real-time detection and tracking will directly affect drivers’ safety.This thesis describes in detail the real-time detection of front-moving vehicles,and elaborates the working process as follows: the first step is preprocessing;the second step is based on feature analysis theory to extract features of vehicles in video images and manually classify positive and negative sample features,then training classification which called the vehicle detector.To meet the real-time and accuracy requirements of tracking in the vehicle tracking part,this thesis uses the KCF vehicle tracking algorithm which can achieve rapid training and recognition and achieve the purpose of fast tracking.Then optimizing and improving the performance of the algorithm.The main contents of this thesis are:(1)Based on HOG+SVM vehicle detection principle,the thesis introduces the spatial Color feature analysis method.By selecting HOG and color combination method,realized the vehicle feature extraction.The edge feature description of HOG is strong,so it can describe the vehicle’s contour.Color characteristics can describe the vehicle’s most superficial features.The change in accuracy of vehicle detection can be clearly seen after adding color features.In order to further improve the detection accuracy,the SVM method is chosen to detect the vehicle in the background of Express way and carry on the real-time tracking.The classification of SVM is simple and easy to use and has good ability in the field of vehicle detection.(2)In view of the KCF algorithm has the advantage of real-time fast tracking.In this thesis,the KCF tracking algorithm is adopted in the vehicle tracking part.Based on the experimental background of Express way,this thesis introduces the KCF tracking algorithm to track the vehicles in front and realizes the fast training,recognition and location.The KCF algorithm uses cyclic shift to generate training samples in the tracking process,so that the training samples can be diagonally based on the discrete Fourier transform and greatly improve the operation speed.A target disappears or is obscured,which often happened in tracking and resulting in failure.The KCF algorithm continuously updates the tracking template in the detection and recognition process,which can effectively reduce the impact of occlusion,deformation and rotation on tracking.At the same time,using the MATLAB environment to complete the whole program design,and to optimize the HOG parameters in detail.(3)In the verification part of detection,this thesis through the video sampling and the experiment data analysis comparison to discover,compared with the original HOG+SVM algorithm,in the detection accurate rate and the accuracy has the obvious enhancement.Therefore,the reliability of this algorithm and the optimization of some degree are also given.In the simulation demonstration part,this thesis takes a video sequence from benchmark as the input of experimental sample,and it is tracking that the KCF algorithm has obvious advantages in tracking speed and accuracy rate.This thesis studies the algorithm has good application value in vehicle tracking field.