一致性特征点匹配在目标跟踪中的应用
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TP391.41

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Target tracking based on consistency feature points matching
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    摘要:

    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。

    Abstract:

    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.

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李静宇,姚志军,田睿.一致性特征点匹配在目标跟踪中的应用[J].电子测量技术,2015,38(10):28-31

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  • 在线发布日期: 2016-03-04
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