摘要
针对传统的均值漂移算法中目标表观模型单一且缺乏必要的更新策略的问题,提出了一种基于多表观模型的多尺度均值漂移跟踪算法.该算法通过对模板集进行稀疏主成分分析获得多个表观模型,并分别在每个模型下以多个尺度并行运行均值漂移算法得到多个收敛点.利用前面求得的多个收敛点求取加权中心,并以此为依据寻找当前时刻的目标状态.实验结果表明,与其他跟踪算法相比,本文提出的算法在应对目标姿态变化、背景干扰及遮挡等复杂情况时具有更好的稳定性和鲁棒性.
Based on multiple appearance models, a novel multi-scale mean shift tracking algorithm was proposed to deal with the problems caused from the relative simplicity of the single appearance model and the absence of the update strategy under the original framework of mean shift tracking. Based on the multiple appearance models which can be obtained by using sparse principal component analysis, numerous converging points were located by running the basic mean shift trackers in parallel. The weighted center was calculated by setting the converging points as the candidate particles, and the best particle was chosen to determine the current state of the object. Experimental results showed that the proposed method was more robust and stable against pose variation, background clutter and occlusion in comparing with other competing tracking models.
出处
《东北大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2013年第10期1374-1377,1382,共5页
Journal of Northeastern University(Natural Science)
基金
国家自然科学基金资助项目(61273078
61005032)
中央高校基本科研业务费专项资金资助项目(N1106040065032)
关键词
目标跟踪
均值漂移
稀疏主成分分析
自适应更新
加权中心
object tracking
mean shift
sparse principal component analysis
adaptive update
weighted center
