摘要
均值漂移(Mean-Shift)目标跟踪算法由于具有快速模板匹配和无参数密度估计等特点,但也存在其固有的缺陷。为了提高该算法的鲁棒性,把目标分成多个区域,对每个区域利用Mean-Shift进行跟踪,迭代次数大于8的放弃迭代。然后利用尺度不变特征变换(SIFT)剔除那些匹配的关键点数目少的子区域。最后,利用匹配关键点数目多的区域得到目标的位置。实验结果表明该方法在目标受遮挡、尺度变化、旋转、环境场景等变化等具有很强的鲁棒性。
Mean-Shift algorithm performs well in object tracking field because of its advantages of fast pattern matching and non-parametric estimation. However, this algorithm has its inherent deficiencies. In order to improve the robustness of Mean-Shift algorithm, the target was divided into a number of sub-regions in this paper, each sub-region individually used Mean-Shift tracking, and those whose iterations are more than eight times quit. And Scale Invariant Feature Transform (SIFT) was employed to exclude those sub-regions with smaller matching key points. Finally, the object location was obtained according to the sub-regions with more matching key points. Experiments show that the proposed method is of high robustness in situations of occlusion, scale change, rotation, scene change, etc.
出处
《计算机应用》
CSCD
北大核心
2009年第10期2678-2680,共3页
journal of Computer Applications
基金
海南省自然科学基金资助项目(60897)
海南省教育厅项目(Hj2009-135)
关键词
目标区域划分
尺度不变特征变换
均值漂移
目标跟踪
target region division
Scale Invariant Feature Transform (SIFT)
Mean-Shift
object tracking