基于代价敏感结构化SVM的目标跟踪

基于结构化SVM的目标跟踪由于其优异的性能而受到了广泛关注，但是现有方法存在正样本和负样本不平衡问题。针对此问题，该文首先提出一种用于目标跟踪的代价敏感结构化SVM模型，其次基于对偶坐标下降原理设计了该模型的求解算法，最后利用提出的代价敏感结构化SVM实现了一种多尺度目标跟踪方法。在OTB100数据集和VOT2019数据集上进行了实验验证，实验结果表明:该文方法相比相关滤波目标跟踪方法，跟踪精度较高，相比深度目标跟踪方法，具有速度优势。

Object Tracking Based on Cost Sensitive Structured SVM

Object tracking based on structured SVM attracts much attention due to its excellent performance. However, the existing methods have the problem of imbalance between positive and negative samples. To solve the problem, a cost sensitive structured SVM model is proposed for object tracking. Secondly, an algorithm for the proposed model is designed via dual coordinate descent principle. Finally, a multi-scale object tracking method is implemented using the proposed cost sensitive structured SVM. The experimental results on OTB100 datasets and VOT2019 datasets show that compared with the correlation filtering trackers, the proposed method has higher tracking accuracy, and has the advantage of speed compared with the deep object trackers.