摘要
为克服视频目标跟踪中仅利用单一特征易导致的跟踪失败,提出了一种基于多特征融合的非线性目标跟踪算法。通过灰度直方图来表征目标的总体分布,利用边缘特征来提取目标的高频细节,将两者融合于粒子滤波的概率模型框架中。并提出一种基于半峰宽和贡献度的特征可信度计算方法,动态调节粒子数目,使可信度高的特征拥有较多的粒子。最后,进行了目标跟踪仿真实验,结果表明,该算法具有较强的抗局部遮挡能力,与单特征跟踪算法相比,平均跟踪误差减小了0.5个像素。
To avoid failing in visual tracking situation when employing single feature, a nonlinear target tracking method based on multi-feature fusion is proposed. Grey histogram is used to describe the overall distribution characteristics of the target and edge feature is employed to extract the high frequency details. The two algorithms are fused in the probabilistic model of particle filter. Feature reliability estimation based on half-band width and contribution is proposed, which provides more reliable features with more particles. In this way, the particle numbers of the features are adjusted dynamically. Compared with single-feature tracking method, the tracking result shows that the algorithm has the strong ability of tracking under local obstruction. The average tracking error of the new algorithm decreases by 0.5 pixels.
出处
《光电工程》
EI
CAS
CSCD
北大核心
2008年第3期5-9,15,共6页
Opto-Electronic Engineering
基金
国家"863"计划项目(2002AA783050)
关键词
多特征融合
特征可信度
灰度直方图
边缘特征
粒子滤波
视频跟踪
multi-feature fusion
feature reliability
grey histogram
edge feature
particle filter
visual tracking
