关键词: 目标跟踪, TLD算法, 关键特征点, 在线位置预测

Abstract: 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.

Key words: target tracking, Tracking-Learning-Detection（TLD）, key feature point, online location prediction