Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (7): 184-192.DOI: 10.3778/j.issn.1002-8331.1901-0398

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Fusion Correlation Particle Filter Object Tracking Algorithm

ZOU Chengming, MING Chenglong, LI Chenglong   

  1. 1.Hubei Key Laboratory of Transportation Internet of Things, Wuhan 430070, China
    2.School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, China
  • Online:2020-04-01 Published:2020-03-28



  1. 1.交通物联网技术湖北省重点实验室,武汉 430070
    2.武汉理工大学 计算机科学与技术学院,武汉 430070


Correlation filter has been widely used in object tracking because of its superiority with efficiency and robustness, but it does not deal with occlusion and scale variation well. A fusion correlation particle filter object tracking algorithm is proposed, more target information and background information can be learned by the multiple correlation filters, it helps to distinguish the target and background better. And the particle filter sampling strategy is presented to capture target quickly when the target leaves the occlusion area. For the scale estimation, multi-scale coefficients are used to determine the scale variation of target, the factor corresponding to the largest response value of candidate region and filter is selected as the scale coefficient. In addition, as the number of particles increases, the computation of particle filtering algorithm also increases, to ease this problem, a re-sampling algorithm based on particle proliferation is proposed, which can make for the tracking efficiency. Finally, three experiments are performed to verify the effectiveness and robustness of the proposed algorithm in dealing with occlusion and scale variation.

Key words: object tracking, correlation filter, particle filter, scale estimation, occlusion



关键词: 目标跟踪, 相关滤波, 粒子滤波, 尺度估计, 目标遮挡