CAJ | 학술논문

为了在航拍视频中准确实时地提取出运动小目标,提出了一种改进后的 ORB 特征匹配和差分相乘算法融合的检测方法.首先,针对原始ORB特征匹配算法出现大量误匹配对的问题,采用基于K最近邻的特征点描述后,对前后两帧特征点进行双向匹配,再通过顺序抽样一致性算法进一步提纯,利用提纯后的匹配点对解算背景运动模型,精确补偿背景运动量,最后利用连续四帧图像差分相乘的方法并经过形态学处理准确分割出航拍视频中的运动小目标.实验结果表明,经过本文算法提纯后匹配对准确度提升到99.9%,平均耗时0.46 s,处理速度约是SURF特征匹配算法的5倍,SIFT特征匹配算法的25倍,能够满足航拍视频实时处理的需求并具有较强的抗噪能力.
위료재항박시빈중준학실시지제취출운동소목표,제출료일충개진후적 ORB 특정필배화차분상승산법융합적검측방법.수선,침대원시ORB특정필배산법출현대량오필배대적문제,채용기우K최근린적특정점묘술후,대전후량정특정점진행쌍향필배,재통과순서추양일치성산법진일보제순,이용제순후적필배점대해산배경운동모형,정학보상배경운동량,최후이용련속사정도상차분상승적방법병경과형태학처리준학분할출항박시빈중적운동소목표.실험결과표명,경과본문산법제순후필배대준학도제승도99.9%,평균모시0.46 s,처리속도약시SURF특정필배산법적5배,SIFT특정필배산법적25배,능구만족항박시빈실시처리적수구병구유교강적항조능력.
In order to extract the small moving target accurately in real time in the aerial video, we propose a fusion detection method for an improved ORB feature matching and differential multiplication algorithm. First of all, as the original ORB appears to be a large number of false matching problems, we describe feature points based on K nearest neighbor. After the description of the feature points in two consecutive frames by two-way matching, we further refine consistency by sequential sampling algorithm. Then, the purified matching points are used to calculate the background motion model, compensating background activity. Finally, the four consecutive frames difference multiplication and morphology processing are used to accurately segment the moving small target in the aerial video. Experimental results show that the match after purification method of accuracy up to 99.9%, the average time of which is 0.46 s, and processing speed is about 5 times of SURF feature matching algorithm, 25 times of the SIFT feature matching algorithm, so it can meet the requirements of aerial video real-time processing and has stronger ability to resist noise.