CAJ | 학술논문

针对背景差分法难以适应光照变化频繁且对实时性要求较高的高速公路监控环境的问题，提出一种差分图像自适应阈值确定算法，利用统计学方法对差分图像中目标的灰度值进行快速有效的分类，并将分类界限作为自适应阈值，再利用差分图像的梯度分布辅助判断运动目标的区域。试验结果表明，该算法可以适应不同的监控环境，能准确识别交通目标，且具有较好的稳定性。
침대배경차분법난이괄응광조변화빈번차대실시성요구교고적고속공로감공배경적문제，제출일충차분도상자괄응역치학정산법，이용통계학방법대차분도상중목표적회도치진행쾌속유효적분류，병장분류계한작위자괄응역치，재이용차분도상적제도분포보조판단운동목표적구역。시험결과표명，해산법가이괄응불동적감공배경，능준학식별교통목표，차구유교호적은정성。
As the existing background subtraction algorithm is insufficient for the real-time expressway monitoring with frequent light change, an adaptive threshold determination algorithm based on the gray level is proposed.In this algorithm, the pixels of the object in the difference image are effectively classified by means of the statistical method, and the classification standard is defined as the adaptive threshold.Moreover, the gradient distribution of the difference image is used to judge the area of motion objects.Experimental results show that the proposed adap-tive threshold algorithm is effective in recognizing objects in different environments with high accuracy and stability.