摘要
传统的基于子空间的跟踪方法易于丢失图像所固有的部分结构和邻域信息,从而降低了目标匹配和跟踪的精度.为此,本文提出了一种增量张量子空间学习算法,用于跟踪目标的建模与模型更新.同时,将该模型与贝叶斯推理相结合,提出一种自适应目标跟踪算法:新方法首先对跟踪目标的外观进行建模,然后利用贝叶斯推理获得目标外观状态参数的最优估计,最后利用最优估计的目标观测更新目标张量子空间.实验结果表明,由于保持了目标外观的结构信息,本文提出的自适应目标跟踪方法具有较强的鲁棒性,在跟踪目标在姿态变化、短时遮挡和光照变化等情况下均可有效地跟踪目标.
The conventional subspaces based tracking methods usually have low precision of object matching and tracking, because they lose the inherent partial structure and neighborhood information. In this paper, an incremental tensor subspace learning algorithm is proposed to model and update the object appearance in tensor subspace. Simultaneously, by combining the proposed learning algorithm with Bayesian inference,an adaptive object tracking method is presented. Firsfly, we represented the appearance of the object in tensor subspace; secondly, obtained the optimal estimation of the state parameters by Bayesian inference;finally up- dated the tensor subspace by using the optimal observation.Due to the construction information is maintained,the proposed method is able to track targets effectively and robustly under pose variation, short-rime occlusion and large lighting and so on in the experiments.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2009年第7期1618-1623,共6页
Acta Electronica Sinica
基金
国家自然科学基金(No.60702061)
教育部长江学者创新团队支持计划(No.IRT0645)
深圳大学ATR国防科技重点实验开放基金
总装备部预研基金(No.9140A06050107DZ0113)
关键词
张量子空间
增量学习
贝叶斯推理
仿射运动
tensor subspace
incremental learning
bayesian inference
affine motion
