Research on video vehicle detection based on AdaBoost classifiers of the ROI
Wang Xiangha · Journal of Liaoning Normal University · 2014
Recently,video-based automatic vehicle detection as a key technology of the urben intelligent transportation system has got more attention.As there are missing detections,false detections, and large amount of calculations of AdaBoost classifier,this paper proposes a detecting algorithm of video-based vehicle based on the motion region extraction of Gaussian mixture model and AdaBoost cascade classifier which has the haar-like features.Firstly,the proposed paper detects the overall area of moving targets by Gaussian mixture model to extract the region of interest(ROI)of vehicle movement.Then,it achieves the detection of the moving vehicles based on the AdaBoost cascade classifier.Because of the application of the detection mode,which is based on the extraction and classification of the motion region,the Gaussian background model can extract the ROI as the candidate region for vehicles accurately.The proposed method restrains the search area of each frame,which makes the target detection by AdaBoost classifier more specifically.In addition,it improves the accuracy and reduces the missing rate of detection.Besides,the proposed method also reduces the scanning time that is required by the slide window of classification algorithm and improves the detecting rate.The experimental results validate the adaptability and availability of the proposed method for the detection of vehicles in complicated traffic environment.