为加强fDSST算法在目标快速运动、快速形变、目标消失情况下的跟踪精度, 提出了一种基于TLD和fDSST的长时间目标跟踪算法。在fDSST算法的基础上, 加入了检测器和学习器对跟踪结果进行修正和学习, 并利用检测器和学习器的正负样本对跟踪结果进行置信度评估, 从而解决了在跟踪失败情况下的错误参数学习问题。实验表明, 基于TLD和fDSST的长时间目标跟踪算法, 不但解决了fDSST算法由于目标快速运动、形变甚至消失而使跟踪失败, 难以进行长时间持续跟踪的问题, 且很大程度上增强了TLD算法的跟踪精度。

In order to improve the tracking precision of the fDSST algorithm when the target is moving fast,deforms or even disappears,a long-term tracking algorithm based on TLD and fDSST is proposed.Based on the fDSST algorithm,a detector and an online learner are used to modify and learn from the tracking results.To solve the problem of learning wrong parameters when the fDSST algorithm fails,the positive and negative samples of the detector and the learner are used to evaluate the tracking results.The experiment results indicate that the long-term tracking algorithm based on TLD and fDSST solves the problem that the fDSST algorithm fails to track the target for a long time when the target is in fast moving,deforms or even disappears.This approach could also improve the tracking precision of the TLD algorithm.