DFST proposes an optimized visual tracking algorithm based on the real-time selection of locally and temporally discriminative features. A feature selection mechanism is embedded in the Adaptive colour Names (CN) tracking system that adaptively selects the top-ranked discriminative features for tracking. DFST provides a significant gain in accuracy and precision allowing the use of a dynamic set of features that results in an increased system flexibility. DFST is based on the unsupervised method "Infinite Feature Selection" (Inf-FS), which ranks features according with their "redundancy" without using class labels. By using a fast online algorithm for learning dictionaries the size of the box is adapted during the processing. At each update, we use multiple examples around the target (at different positions and scales). DFST also improved the CN by adding micro-shift at the predicted position and bounding box adaptation.

DFST提出了一种基于实时选择本地和时间上有区别的特征的优化视觉跟踪算法，该算法嵌入了自适应颜色名称（CN）跟踪系统中的特征选择机制，同时采用无监督方法“Infinite Feature Selection”（Inf-FS）来排名特征，通过使用快速在线算法来学习字典，实现了特征选择和跟踪的动态设置。

动态特征选择过程中的目标跟踪