LI Ying, LI Jing-yu, XU Zheng-ping. Moving Target Fast Tracking Using SURF and Cluster Analysis Method[J]. , 2011,26(4): 544-550 DOI: 10.3788/YJYXS20112604.0544.
提出了结合SURF(Speed Up Robust Features)与K-means聚类分析的运动目标快速跟踪算法(SURF-KMs)
对图像的局部多尺度特征提取与描述进行了研究。首先
使用SURF算法在跟踪窗口内提取特征点
生成并匹配特征矢量。然后
利用K-means算法估计目标特征点的质心位置
确定其聚集范围
实时更新窗口尺寸和位置。最后
建立目标模板更新策略
当目标发生形态变化而无遮挡时
更新目标模板。实验结果表明
当目标发生大角度旋转和快速缩放
同时发生颜色变化时
所提出的SURF-KMs算法仍能够实现稳定的跟踪
且满足运动目标实时跟踪的稳定可靠、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
specially when the shape of moving objective or its environment condition change
a new approach named SURF-KMs which combines the advantage of SURF algorithm with a cluster analysis of K-means method is proposed. First
based on SURF algorithm
interest points and vectors are presented. Second
using K-means method
we can estimate the target-center
determine targets cluster scope and update the tracking window online. Finally
a self-adapting updating strategy for matching template is also proposed in order to track moving target automatically. Experimental results indicate that SURF-KMs is mostly able to achieve a stable tracking while 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.
关键词
Keywords
references
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