：针对目标跟踪过程中目标可能出现的快速变化和严重遮挡等问题，提出了一种基于新的子空间表示的目标跟踪算法。采用距离不变量对尺度不变特征变换(SIFT)特征点匹配对进行提纯。用提纯后的特征点匹配对，通过线性拟合得到仿射变化参数。在粒子滤波的理论框架下，采用快速的迭代算法，建立目标的主分量(PCA)子空间表示，结合计算得到的仿射变化参数，构造有效的目标观测模型完成跟踪。同时，采用在线学习的方法对SIFT特征点和PCA子空间进行定时更新。大量实验表明，提出的算法能快速有效地完成对姿态和形状剧烈变化的目标的精确跟踪。

A new subspace-based tracking algorithm is proposed to deal with rapid changes and severe occlusion of targets during target tracking. Distance invariants are used to select superior pairs of SIFT features. Affine transformation parameters of affine transformation are calculated via linear fitting using these superior pairs. PCA subspace representation of the target is calculated via quick iterations. An effective observation model is constructed to track targets by combining this representation with obtained parameters under the framework of particle filter tracking. And SIFT features and PCA subspace are updated via online learning regularly. At the end, lots of experiments are performed and it is certificated that the proposed algorithm is able to precisely track targets of which gestures and shapes vary rapidly.