针对运动目标鲁棒跟踪问题，提出一种基于离线字典学习的视频目标跟踪鲁棒算法。采用字典编码方式提取目标的局部区域描述符，随后通过训练分类器将跟踪问题转化为背景和前景分类问题，最终通过粒子滤波对物体位置进行估计实现跟踪。该算法能够有效解决由于光照变化、背景复杂、快速运动、遮挡产生的跟踪困难。经过不同图像序列的实验对比表明，与现有方法相比，本文算法的鲁棒性较高。

Focusing on the problem of robust object tracking, we propose an object-tracking algorithm based on dictionary learning and practical filter. We represent local image patches of a target object by codes with offline dictionary learning and pose object tracking as a binary classification problem.A learned classifier and a practical filter are used to estimate the tracking result. The proposed method can solve the difficulty caused by illumination variations, complex backgrounds,occlusions and so on.We do the simulations on a variety of challenging sequences. Experiments on different sequences with evaluation of the state-of-the art methods show our algorithm performs favorable performance.