CAJ | 학술논문

针对混合交通流中两轮车辆视频检测问题，提出一种基于混合高斯模型（GMM）与背景累加模型（BAM）的组合前景提取方法，该方法将GMM与BAM组合得到的2种前景图像分别经过滤波和形态学的膨胀操作处理，然后进行“与”操作，过滤掉高斯前景中的大量噪声，提取出感兴趣前景区域.针对两轮车辆的轮廓边缘特征，采用Canny边缘检测提取边缘信息，去除前景区域中的非目标区域，采用两轮车辆的自建模板，通过欧氏距离进行模板匹配，定位并标记感兴趣区域中的目标区域.在OpenCV和Matlab7.1实验测试平台上，对典型城市混合交通路段的交通流视频进行测试.结果表明，该方法对混合交通流中两轮车辆的识别检测具有较高的准确率.
침대혼합교통류중량륜차량시빈검측문제，제출일충기우혼합고사모형（GMM）여배경루가모형（BAM）적조합전경제취방법，해방법장GMM여BAM조합득도적2충전경도상분별경과려파화형태학적팽창조작처리，연후진행“여”조작，과려도고사전경중적대량조성，제취출감흥취전경구역.침대량륜차량적륜곽변연특정，채용Canny변연검측제취변연신식，거제전경구역중적비목표구역，채용량륜차량적자건모판，통과구씨거리진행모판필배，정위병표기감흥취구역중적목표구역.재OpenCV화Matlab7.1실험측시평태상，대전형성시혼합교통로단적교통류시빈진행측시.결과표명，해방법대혼합교통류중량륜차량적식별검측구유교고적준학솔.
A two-wheel vehicle detection method for mixed traffic flow based on combination foreground extraction method is proposed. The foreground images extracted by Gausssian Mixture Model (GMM) and Background Accumulate Model (BAM) are carried out with“and”operation after the operations of filtration and expansion of morphology, which can filter out a lot of noise in the GMM foreground and extract the interested foreground fields. According to the contour edge character of two-wheel vehicles, the Canny edge detection method is used to extract the edge information. The self-built template and Euclidean distance are used in pattern matching in order to locate and mark the target area. The experiments are performed using the traffic flow video in the classical urban mixed traffic road. The results show that this method has high accuracy of two-wheel vehicle detection.