• DocumentCode
    724534
  • Title

    Improved hierarchical association model based mult-target tracking

  • Author

    Xiangxiang Li ; Songhao Zhu ; Lingling Chen ; Zhe Shi

  • Author_Institution
    Sch. of Autom., Nanjing Univ. of Posts & Telecommun., Nanjing, China
  • fYear
    2015
  • fDate
    23-25 May 2015
  • Firstpage
    5196
  • Lastpage
    5201
  • Abstract
    To deal with the issue of multi-target tracking, this paper proposes a hierarchical correlation multi-target tracking trajectory generation method. On the basis of target detection and initial trajectory, the AdBoost algorithm combined with online discriminant analysis apparent model is first utilized to achieve initial tracking trajectories; then, the Hungarian algorithm is here utilized to optimize fragmented and discontinuous tracking trajectories to achieve stable and accurate trajectories fragments; finally, the intelligent extrapolation based on energy minimization here utilized to achieve the final smoother and longer tracking trajectories. Experimental results on PETS 2009/2010 benchmark and TUD-Stadtmitte video database demonstrate the effectiveness and efficiency of the proposed scheme.
  • Keywords
    learning (artificial intelligence); object detection; optimisation; statistical analysis; target tracking; AdaBoost algorithm; Hungarian algorithm; hierarchical association model; multitarget tracking trajectory generation method; online discriminant analysis apparent model; target detection; tracking trajectory optimization; Decision support systems; Adboost Algorithm; Continuous Energy Minimization; Hierarchical Correlation; Hungarian Algorithm; Online Discriminant Analysis Apparent Model;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control and Decision Conference (CCDC), 2015 27th Chinese
  • Conference_Location
    Qingdao
  • Print_ISBN
    978-1-4799-7016-2
  • Type

    conf

  • DOI
    10.1109/CCDC.2015.7162851
  • Filename
    7162851