判别稀疏表示与在线字典学习的运动目标跟踪

针对传统稀疏表示不能有效区分目标和背景的缺点，提出一种判别稀疏表示算法，这种算法在传统稀疏表示目标函数中加入一个判别函数，大大降低干扰因素对目标跟踪的影响。基于判别稀疏表示和[?1]约束，提出一种在线字典学习算法升级目标模板，有效降低背景信息对目标模板的影响。提取目标梯度方向的直方图（HOG）特征，利用其对光照和形变等复杂环境具有较强鲁棒性的优点，实现对目标更稳定的跟踪。实验结果表明，与现有跟踪方法相比，该算法的跟踪效果更好。

Discriminative sparse representation and online dictionary learning for visual tracking

 Traditional sparse representation can not effectively distinguish between target and background. Aiming at these problems, a discriminative sparse representation is proposed. It adds a discriminative function to the traditional sparse, thereby greatly reducing the influence of interference factors. An online dictionary learning algorithm based on discrimination sparse representation and [?1] constraint is proposed to upgrade target template. It can effectively reduce the impact of the target and the background of the target template. In addition, Histograms of Oriented Gradient（HOG）feature is used to represent the target. The advantage is its robustness to illumination changes. The proposed tracker is empirically compared with state-of-the-art trackers on some challenging video sequences. Both quantitative and qualitative comparisons show that the proposed tracker is superior and more stable.