Research On Vehicle Detection And Tracking For On-board Monocular Vision Based On Active Learning

With the rapid growth of car ownership,road traffic accidents have become the world's increasingly serious public safety issues that to be resolved.The Forward collision warning system is an important part of the advanced driver assistant system,which can effectively reduce the probability of road traffic accidents.The accuracy,continuity and real-time performance of vehicle detection and tracking are the decisive factors that affect the function of the system.Among them,the accuracy and continuity of vehicle positioning is the prerequisite for early warning function,and real-time warning function is the key to effective play,so that enables drivers' early detection of danger.Therefore,this dissertation is devoted to on-board monocular vision vehicle detection and tracking algorithm research,the specific research content is as follows:Based on Active Learning Classifier Model Training.Monocular vision vehicle detection based on machine learning requires a large number of sample data with labels to train a classifier model that can accurately classify vehicles and backgrounds in images.This dissertation,an active learning algorithm based on misclassified query strategy is proposed,which obtains the most informative of the sample data with smaller manual annotation cost,and iterative training optimizes the performance of the classifier.Adaboost Cascade Multi-target Vehicle Detection.In order to improve the accuracy of vehicle detection,this dissertation presents a sub-regional multi-classifier vehicle detection method.According to the difference of the vehicle features in the detection field of view,the vehicle to be detected is divided into forward vehicles,left oblique lateral vehicles and right oblique lateral vehicles,respectively,to train the cascade classifier to detect.At the same time,in order to improve the real-time performance of the detection,a multi-scale vehicle detection acceleration algorithm combined with camera calibration is proposed to detect the different down-sampling images in different ways.HOG Feature Tracking and Adaboost Detection Fusion.Aiming at the problem that Adaboost cascade detection is not continuous enough,a vehicle detection and tracking algorithm based on Adaboost cascade detection and HOG feature tracking is proposed.Through the HOG feature tracking integration,increased by about 10%of the true positive rate,so that the detection results more continuous.Design and Implementation of Forward Collision Warning System.At the end of this dissertation,a forward collision warning system is designed by using the vehicle detection and tracking algorithm.The real-time,accurate and continuous detection and tracking the forward vehicle and calculate the distance from the front vehicle in real time.Timely warning signal,so as to avoid the occurrence of traffic accidents.