吉林大学学报(工学版) ›› 2018, Vol. 48 ›› Issue (2): 533-538.doi: 10.13229/j.cnki.jdxbgxb20160411

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Target tracking based on improved accelerated gradient under tracking-learning-detection framework

YANG Xin1, 2, XIA Si-jun1, LIU Dong-xue1, FEI Shu-min3, HU Yin-ji2   

  1. 1.College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;
    2.Key Laboratory of Photoelectric Control Technology, Luoyang 471000, China;
    3.School of Automation, Southeast University, Nanjing 210096, China
  • Received:2016-04-05 Online:2018-03-01 Published:2018-03-01

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Abstract: 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.

Key words: artificial intelligence, target tracking, tracking-learning-detection (TLD), occlusion, drift

CLC Number: 

  • TP391.41
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