基于关键特征点的改进TLD目标跟踪算法研究

TLD（Tracking-Learning-Detection）算法是一种新颖的单目标长时间视觉跟踪算法，在给定极少的先验知识的情况下，能够迅速地学习目标特征并进行有效的跟踪。TLD算法中跟踪器每次在跟踪目标上均匀地选取特征点进行跟踪，不能保证每个特征点都能够被可靠地跟踪。针对这个问题，提出一种基于关键特征点检测的改进TLD算法，保证所选特征点都能够被正确可靠地跟踪，防止跟踪结果发生漂移，提高了跟踪器的跟踪精度。另一方面，在TLD检测器中引入了基于轨迹连续性的在线位置预测，在保证正确跟踪的前提下，缩小了检测器的检测范围，提高了运算速度。实验结果表明，该算法有较高的跟踪精度和速度。

Improved TLD target tracking algorithm based on key feature points

Tracking-Learning-Detection（TLD） tracking algorithm is a novel long time single-target tracking algorithm. It can learn object features quickly and track effectively with very little prior knowledge given. However, the tracker in TLD tracking algorithm selects the target feature points uniformly each time, which cannot ensure that each of the feature points can be reliably tracked. To solve this problem, an improved TLD algorithm is proposed, based on key feature point detection. The proposed algorithm can ensure that the selected feature points can be tracked reliably and correctly. Drift tracking is prevented and tracking accuracy is improved. Furthermore, online location prediction based on trajectory continuity is introduced into the TLD detector, which can not only ensure proper tracking but also reduce the detection range of the detector. What’s more, the computing speed is improved. Experimental results show that the proposed algorithm has high tracking accuracy and computing speed.