In this paper, online non-negative discriminative dictionary learning for tracking is proposed, which combines the advantages of the global dictionary learning model and the class-specific dictionary learning model. The previous algorithm based on general dictionary learning does not take into account the inter-class relations between classes and make full use of tag information. In order to improve the classification ability of dictionaries, the class correlation was proposed to guide the learning of discriminant dictionaries, which makes full use of the correlation and difference between the atomic classes of dictionaries and introduces the tag information of the categories to improve the discriminant ability of dictionaries. For this purpose, the Huber loss function and the Fisher weight coefficient is used in the discriminative term to improve computational efficiency. In addition, non-negative constraints is added on dictionaries to enhance the performance. The OTB50 and OTB100 datasets are used to evaluate our tracker and compare with related algorithm. The experimental results show that our method performs much better than the tracking method compared in this paper.

本文提出了一种用于跟踪的在线非负判别词典学习方法，该方法结合了全局词典学习模型和特定类词典学习模型的优点。先前基于通用字典学习的算法没有考虑类之间的类间关系，而是充分利用标签信息。为了提高词典的分类能力，提出了类别相关性，以指导判别词典的学习，充分利用词典的原子类之间的相关性和差异性，引入类别的标签信息来提高判别能力。字典的能力。以此目的，在判别式中使用了Huber损失函数和Fisher加权系数来提高计算效率。另外，在字典上添加了非负约束以增强性能。OTB50和OTB100数据集用于评估我们的跟踪器并与相关算法进行比较。实验结果表明，与本文的跟踪方法相比，我们的方法具有更好的跟踪性能。