CAJ | 학술논문

为了解决运动目标快速跟踪过程的实时性与稳定跟踪问题，提出了一种新的基于局部特征点匹配的 KPM (key points matching)算法，对图像的局部多尺度特征提取与匹配进行研究。首先，应用 SURF(speeded up robust features)算法在跟踪窗口内提取特征点，生成并匹配特征矢量。然后，结合最近邻提纯法与一致提纯法剔除目标区域以外的特征点对，减少误匹配以提高跟踪精度。最后，生成目标仿射变换矩阵，更新目标运动参数。实验结果表明，本文所提出的 KPM 算法当目标发生大角度旋转和快速缩放，同时发生光照变化时，仍能够实现稳定的跟踪，且满足运动目标实时跟踪稳定可靠、精确度高、抗干扰能力强等指标要求。
위료해결운동목표쾌속근종과정적실시성여은정근종문제，제출료일충신적기우국부특정점필배적 KPM (key points matching)산법，대도상적국부다척도특정제취여필배진행연구。수선，응용 SURF(speeded up robust features)산법재근종창구내제취특정점，생성병필배특정시량。연후，결합최근린제순법여일치제순법척제목표구역이외적특정점대，감소오필배이제고근종정도。최후，생성목표방사변환구진，경신목표운동삼수。실험결과표명，본문소제출적 KPM 산법당목표발생대각도선전화쾌속축방，동시발생광조변화시，잉능구실현은정적근종，차만족운동목표실시근종은정가고、정학도고、항간우능력강등지표요구。
In order to design a moving target fast tracking system with respect to a time-limited and stable tracking process,especially when the shape of moving objective or its environment condition change,a new approach based on matching local feature points named KPM (key points matching)is first proposed,and local Multi-scale feature extraction and matching technology for images are also been researched.First,based on SURF (speeded up robust features)algorithm,interest points and vectors are presented.Second,combine nearest purifying and consistency purifying to move out features outside the target area,so that we can decrease the failed matching and improve the tracking precision.Finally,generate target affine transform matrix and update the moving parameter of the target. Experimental results indicate that KPM is mostly able to achieve a stable tracking while with the monitored target rotating,scale changing, and also the environment illumination glittering. Moreover, it can satisfy the system requirements of tracking stability,higher precision and anti-jamming.