摘要
提出一种在复杂场景及目标遮挡情况下,特别是目标外形、大小发生变化时的基于SURF的目标跟踪和在线目标模型更新算法。该算法利用SURF对尺度缩放、光照变化和旋转等具有较好鲁棒性的特点,首先提取跟踪目标的SURF特征点,以特征点及其邻域的R、G、B直方图表示目标;然后根据目标在连续的帧中相似性较大的特点,搜索当前帧中的目标最优匹配SURF特征点,由目标特征及目标模型计算其准确大小和位置,并根据变化了的目标状态更新目标模型。实验结果表明,该算法可准确地定位到目标。
This paper presents a SURF based target tracking and online object model update algorithm in complex scenarios and target shade,especially in the variation of target outline andscale.The algorithm initially extracts SURF points of the tracked target and RGB histograms of them and their adjacent areas with the robustness of SURF to image zoom,illumination variation and rotation.It then searches optimal matching SURF points in the current frame based on such characteristic as higher target similarity among the sequential frames.It calculates the exact position and size of the target according to the target model and its features,and then updates the target model based on the varied object status.Experimental results show that the algorithm can accurately localize a target.
出处
《山东科学》
CAS
2014年第4期75-84,共10页
Shandong Science
基金
山东省自然科学基金(ZR2010FM004)
山东中医药大学"名科工程"青年骨干培养计划(ZYDXY1358)
