CAJ | 학술논문

近年来基于视频的车辆自动检测作为城市智能交通系统的一项重要技术一直受到关注。针对AdaBoost分类器目标检测所存在的漏检、误检和计算量过大等问题，提出一种基于混合高斯模型运动区域提取和 Haar-like特征的AdaBoost级联分类器的交通视频车辆检测算法，首先通过建立混合高斯模型对运动目标的总体区域进行检测，进而提取基于车辆运动的感兴趣区域，再对其进行基于 Haar-like特征的区域AdaBoost 级联分类，实现对运动车辆的检测。由于采用了基于运动区域提取和分类相结合的检测模式，通过混合高斯背景模型较准确的提取出ROI作为车辆的候选区域，约束了每帧的搜索区域，使AdaBoost分类器的目标检测更具针对性，提高了检测的准确性，降低了漏检率；同时也减少了分类算法滑动窗口扫描所需要的时间，提高了检测速度。实验结果验证了所提出算法对复杂交通环境车辆检测的适应性和有效性。
근년래기우시빈적차량자동검측작위성시지능교통계통적일항중요기술일직수도관주。침대AdaBoost분류기목표검측소존재적루검、오검화계산량과대등문제，제출일충기우혼합고사모형운동구역제취화 Haar-like특정적AdaBoost급련분류기적교통시빈차량검측산법，수선통과건립혼합고사모형대운동목표적총체구역진행검측，진이제취기우차량운동적감흥취구역，재대기진행기우 Haar-like특정적구역AdaBoost 급련분류，실현대운동차량적검측。유우채용료기우운동구역제취화분류상결합적검측모식，통과혼합고사배경모형교준학적제취출ROI작위차량적후선구역，약속료매정적수색구역，사AdaBoost분류기적목표검측경구침대성，제고료검측적준학성，강저료루검솔；동시야감소료분류산법활동창구소묘소수요적시간，제고료검측속도。실험결과험증료소제출산법대복잡교통배경차량검측적괄응성화유효성。
Recently ,video-based automatic vehicle detection as a key technology of the urben intelli-gent 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 w hich 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 move-ment .Then ,it achieves the detection of the moving vehicles based on the AdaBoost cascade classifi-er .Because of the application of the detection mode ,which is based on the extraction and classifica-tion of the motion region ,the Gaussian background model can extract the ROI as the candidate re-gion 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 accu-racy and reduces the missing rate of detection .Besides ,the proposed method also reduces the scan-ning time that is required by the slide window of classification algorithm and improves the detecting rate .T he experimental results validate the adaptability and availability of the proposed method for the detection of vehicles in complicated traffic environment .