CAJ | 학술논문

针对现有视频车辆检测算法受光照、噪声等环境因素影响大,漏检和误检率高,难以同时满足鲁棒性及实时性的问题,提出了一种完整的前方车辆检测算法.该算法在改进的Hough变换提取车道线的基础上,首先对图像进行自适应二值化处理,通过腐蚀、膨胀法滤除干扰点；使用简洁有效的方法进行阴影线的合并及ROI区域的提取；算法利用目标区域内的信息熵、车尾对称性特征对感兴趣区域( region of interest, ROI)进行筛选和判别,降低了算法的漏检和误检率；使用改进的Robinson方向检测算子提取车辆边界,取得了较好的效果.结果表明：在处理分辨率为640伊480的视频时,检测正确率89%,运算速度平均为17.6帧/s.
침대현유시빈차량검측산법수광조、조성등배경인소영향대,루검화오검솔고,난이동시만족로봉성급실시성적문제,제출료일충완정적전방차량검측산법.해산법재개진적Hough변환제취차도선적기출상,수선대도상진행자괄응이치화처리,통과부식、팽창법려제간우점；사용간길유효적방법진행음영선적합병급ROI구역적제취；산법이용목표구역내적신식적、차미대칭성특정대감흥취구역( region of interest, ROI)진행사선화판별,강저료산법적루검화오검솔；사용개진적Robinson방향검측산자제취차량변계,취득료교호적효과.결과표명：재처리분변솔위640이480적시빈시,검측정학솔89%,운산속도평균위17.6정/s.
Existing algorithms of video vehicle detection can be affected by light, noise and other environmental factors with high missed and false detection rate, and it is also difficult to meet robust and real-time, a complete algorithm of front vehicle detection was presented. On the basis of the improved Hough transform extracting the lane, this algorithm first processed the image by adaptive binarization, filtering out interference point through method of corrosion and expansion. Shaded area was merged and the ROI was extracted in simple and effective way. This algorithm could utilize entropy and rear symmetry characteristics to screen and discern the ROI area, reducing the missing and false detection rate. Good results were achieved by using Robinson-direction detection operator to extract the boundary of vehicle. Results show that when the video has a resolution of 640 í480 pixels, the correct recognition rate has achieved 89.4%, and there will be 17.65 frame to be processed within per second.