中文摘要：

针对基于L2范数的主分量分析（L2-PCA）易受离群数据的影响，使得传统的基于L2—PCA的视觉跟踪对目标遮挡的鲁棒性较差的问题，提出一种基于L1范数最大化主分量分析（PCA—L1）的视觉跟踪算法．利用PCA—L1对目标表观建模，以粒子滤波为框架估计目标的状态；为了适应目标变化并克服“模型漂移”问题，提出一种PCA—L1的在线更新方法以实现子空间的更新．通过实验验证并与现有算法进行了比较的结果表明，文中算法具有较优的跟踪性能．

英文摘要：

The principal component analysis based on L2-norm （L2-PCA） is sensitive to outliers, which result in the visual tracking algorithms based on L2-PCA having lower robustness to occlusions. To alleviate this problem, a novel visual tracking algorithm via Ll-norm maximization principal component analysis （PCA-L1） is proposed in this paper. The proposed algorithm models the object appearance using PCA-L1 , and infers the states of object with particle filter. In addition, to adapt to changes of object appearance and avoid model drifting, an online PCA-L1 update method is proposed. The experimental results on several challenging sequences show that the proposed algorithm has better performance than that of the state-of-the-art tracker.