Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Multi-target tracking by enhancing the kernelised correlation filter-based tracker

A new tracking method based on the kernelised correlation filter (KCF) method is proposed. The tracker improves KCF-based trackers by adding seven proposed components, namely, the motion model, background subtraction, occlusion handling, hijacking handling, object proposal, bounding box modification, and object re-detection. With these components, the tracker robustly tracks multiple targets despite severe occlusion, rapid motion, and the presence of other objects with similar appearance. The visual tracking performance is evaluated by using challenging basketball game videos. Experiments demonstrate that the tracker outperforms the original KCF tracker and other state-of-the-art tracking methods.

References

    1. 1)
      • 6. Kim, K., Kwon, J., Cho, K.: ‘Multi-object tracker using kernelized correlation filter based on appearance and motion model’. ICACT, Pyeongchang, Korea, February 2017.
    2. 2)
    3. 3)
      • 2. Jia, X., Lu, H., Yang, M.H.: ‘Visual tracking via adaptive structural local sparse appearance model’. CVPR, Providence, RI, USA, June 2012.
    4. 4)
      • 1. Hare, S., Saffari, A., Torr, P.: ‘Struck: structured output tracking with kernels’. ICCV, Barcelona, Spain, November 2011.
    5. 5)
      • 4. Zhang, J., Ma, S., Sclaroff, S.: ‘MEEM: robust tracking via multiple experts using entropy minimization’. ECCV, Zurich, Switzerland, September 2014.
    6. 6)
      • 7. Kwon, J., Grygoriev, A., Lim, Y., et al: ‘Edge fields for robust object proposal’. ISIS, Mokpo, Korea, November 2015.
    7. 7)
      • 3. Kwon, J., Lee, K.M.: ‘Visual tracking decomposition’. CVPR, San Francisco, CA, USA, June 2010.
http://iet.metastore.ingenta.com/content/journals/10.1049/el.2017.2129
Loading

Related content

content/journals/10.1049/el.2017.2129
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address