A TLD tracking algorithm based on scale-adaptive mean-shift method

被引：0
|
作者：
Zhang J.-L. [1 ,2 ]
Shi P. [1 ]
Wen X.-B. [3 ]
机构：
[1] School of Electrical and Electronics Engineering, Tianjin University of Technology, Tianjin
[2] Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems, Tianjin
[3] Key Laboratory of Computer Vision and System of Ministry of Education of China, Tianjin University of Technology, Tianjin
来源：
Kongzhi yu Juece/Control and Decision | 2019年 / 34卷 / 01期
关键词：
Mean-shift; Scale-adaptive; Tracking-detection feedback; Tracking-learning-detection;
D O I：
10.13195/j.kzyjc.2017.1029
中图分类号：
学科分类号：
摘要：
In order to solve tracking failures caused by objects deformation, occlusion and fast motion, an algorithm called mean shift-tracking learning detection(MS-TLD) under the framework of the classical tracking-learning-detection(TLD) algorithm is proposed, which reconstructs a new tracker using the scale-adaptive mean-shift method. By introducing color histogram features and scale-adaption, the new tracker can track objects with deformation and fast moving. A new tracking-detection feedback strategy for the inter-correction between tracker and detector is designed, by which the proposed algorithm has better robustness when objects are occluded. The TB-50 standard dataset is used to verify and evaluate the proposed method. The experimental results show that the proposed algorithm can overcome the tracking failures caused by objects with deformation, occlusion, fast motion, as well as background clutters, and has better tracking accuracy and robustness compared with the TLD and other 3 classical algorithms. © 2019, Editorial Office of Control and Decision. All right reserved.
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页码：144 / 150
页数：6
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