中文摘要：

传统子空间跟踪较好解决了目标表观变化和遮挡问题,但其仍存在对复杂背景下目标跟踪鲁棒性不足和模型漂移等问题.针对这两个问题,本文首先通过增大背景样本的重构误差和利用L1范数损失函数建立一种在线鲁棒判别式字典学习模型;其次,利用块坐标下降设计了该模型的在线学习算法用于视觉跟踪模板更新;最后,以粒子滤波为框架,结合提出的模板更新方法实现了鲁棒的视觉跟踪.实验结果表明：与IVT（Incremental Visual Tracking）、L1APG（L1-tracker using Accelerated Proximal Gradient）、ONNDL（Online Non-Negative Dictionary Learning）和PCOM（Probability Continuous Outlier Model）等典型跟踪方法相比,本文方法具有较强的鲁棒性和较高的跟踪精度.

英文摘要：

The traditional subspaces based visual trackers well solved appearance changes and occlusions. However, they were weakly robust for complex background and prone to model drifting. To deal with these two problems, this paper enlarges reconstruction errors of the background samples and uses Ll-norm loss function to establish an online robust dis- crimination dictionary learning model. Then an online robust discrimination dictionary learning algorithm for template upda- ting in visual tracking is designed via the block coordinate descent （BCD）. Finally, robust visual tracking is achieved with the proposed template updating method in particle filter framework. The experimental results show that the proposed method has better performance in robustness and accuracy than the state-of-the-art trackers such as IVT （Incremental Visual Track- ing ）, L1 APG （ L1 -tracker using Accelerated Proximal Gradient ）, ONNDL （ Online Non-Negative Dictionary Learning ） and PCOM（ Probability Continuous Outlier Model）.