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基于关键特征点的改进TLD目标跟踪算法研究 被引量：10

Improved TLD target tracking algorithm based on key feature points
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摘要 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。 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.
作者 秦飞 汪荣贵 梁启香 张冬梅 李想
机构地区 合肥工业大学计算机与信息学院
出处 《计算机工程与应用》 CSCD 北大核心 2016年第4期181-187,共7页 Computer Engineering and Applications
基金 安徽省自然科学基金项目(No.1408085QF117) 中央高校基本科研业务费专项资金资助(No.2012HGCX0001) 国家自然科学基金(No.61075032)
关键词 目标跟踪 TLD算法 关键特征点 在线位置预测 target tracking Tracking-Learning-Detection（TLD） key feature point online location prediction
分类号 TP391.41 [自动化与计算机技术—计算机应用技术]
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参考文献22

  • 1Lepetit V,Lagger P,Fua P.Randomized trees for real-time keypoint recognition[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR),2005(2):775-781.
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二级参考文献20

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  • 10Avidan S. Ensemble tracking[ J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29 (2) :261-271.

共引文献46

同被引文献53

引证文献10

二级引证文献35

计算机工程与应用
2016年 第4期

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