标题：
Target tracking based on improved accelerated gradient under tracking-learning-detection framework
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所属单位：
自动化学院
发表刊物：
Jilin Daxue Xuebao (Gongxueban)
摘要：
The main challenges in a single target persistent tracking are the factors such as the change of the target pose, similar background and occlusion, which account for the difficulty in solving the drift problem. Based on Tracking-Learning-Detection (TLD), we propose an improved Li Tracker Using Accelerated Proximal Gradient Approach (L1APG) tracking algorithm. First, we add occlusion detection to the L1APG tracker. Then, we transform the occlusion problem into the weight of the target template and the background template coefficients. Finally, the original tracker is replaced by the improved L1APG tracker in the traditional TLD algorithm, the coefficient weights vary adaptively according to the degree of occlusion in real time, which contributes to improving the tracking results effectively. Experiments show that, compared with TLD and L1 tracking algorithms, the proposed algorithm can better deal with the occlusion and drift problems and possesses better stability and robustness. © 2018, Editorial Board of Jilin University. All right reserved.
ISSN号：
1671-5497
是否译文：
否
发表时间：
2018-03-01
合写作者：
Xia, Si-Jun,Liu, Dong-Xue,Fei, Shu-Min,Hu, Yin-Ji
通讯作者：
杨欣
发表时间：
2018-03-01
学位：工学博士学位
职称：副教授
所在单位：自动化学院