Research On The Vehicle Detection Method Based On The Suggestion Window And The ADABOOST Classifier

With the development of economy and the improvement of people's living standards,there is a sharp increase in car ownership,and the traffic accidents are increasing year by year,road traffic safety has become an issue with great concern of the whole society.Therefore the advanced driver assistance system is widely recognized by researchers.Vehicle detection is the important part of advanced driver assistance system,which can alert potential dangers ahead and ensure safety.Based on image processing technology,this thesis proposed a vehicle detection method based on Edge Boxes and AdaBoost to solve the problem of low detection rate and slow detection speed in the current vehicle detection method.Firstly,two hundred proposal windows are obtained by Edge Boxes method according to the edge feature.Secondly,the corresponding relation between vehicle window size and longitudinal distance is obtained by camera calibration method.The camera calibration technique is used to filter windows which do not contain the vehicle,then clustering and sorting the windows that obtained by filter operation.Finally,a few top scored windows of each cluster are selected and fed into the AdaBoost classifiers for vehicle detection.In order to improve the detection precision,the boundary compaction method is used to process the classification results.Experiments with traffic video in different weather and road conditions verify the accuracy,robustness and speed of the proposed method.