由于传统的子空间方法易于丢失图像目标的二维特性，为此本文提出了一种新颖的自适应目标跟踪算法，通过张量的方式建立目标的外观模型——张量子空间，利用在线学习的方法更新其外观模型，同时，利用目标仿射运动的先验信息，通过粒子滤波自适应地跟踪运动目标，并将获得的最优目标观测作为新数据反馈回子空间更新.此外，为了保证子空间更新能获得精确且紧致的目标子空间表达，引入动态部分函数滤除样本野点.实验结果表明，本文提出的自适应目标跟踪方法具有较强的鲁棒性，对于存在姿态变化、短时遮挡和光照变化等情况下均可有效地跟踪目标.

In order to overcome the disadvantage that traditional subspace methods usually lose the two-dimensional information of the objects in image,a novel adaptive object tracking method is proposed.The appearance of the object in tensor subspace is modeled and the object model is updated with online learning method.The object is tracked by using particle filter and the prior of affine motion,and the optimal observation is feeded back to the tensor subspace updating.Moreover,DPF is introduced into the subspace updating to reject outliers so as to keep the object subspace precise and compact.The proposed method is able to track targets effectively and robustly under pose variation,short-time occlusion and large lighting and so on in the experiments.