Improvement of the KCF Tracking Algorithm through Object Detection

  • Abstract
  • Keywords
  • References
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  • Abstract

    When the position of the beam projector is changed, users have to manually adjust the position. In this paper, we propose a system that can automatically correct images. In this process, the KCF (Kernelized Correlation Filter) algorithm is used for tracking the IR (Infrared) markers. We analyze the object tracking failure problem of the KCF and improve the KCF tracking algorithm that solves the problem through object detection.



  • Keywords

    Detection, Tracking, KCF algorithm, Color space, Histogram.

  • References

      [1] Santo, Hiroaki, Takuya Maekawa, and Yasuyuki Matsushita, Device-free and privacy preserving indoor positioning using infrared retro-reflection imaging, Pervasive Computing and Communications (PerCom), IEEE International Conference on. IEEE (2017), 141-152

      [2] Parekh, Himani S., Darshak G. Thakore, and Udesang K. Jaliya, A survey on object detection and tracking methods, International Journal of Innovative Research in Computer and Communication Engineering 2.2 (2014), 2970-2979.

      [3] Joshi, Kinjal A., and Darshak G. Thakore, A survey on moving object detection and tracking in video surveillance system, International Journal of Soft Computing and Engineering 2.3 (2012), 44-48.

      [4] Kalal, Zdenek, Krystian Mikolajczyk, and Jiri Matas, Tracking-learning-detectio, IEEE transactions on pattern analysis and machine intelligence 34.7 (2012), 1409-1422.

      [5] Held, David, Sebastian Thrun, and Silvio Savarese, Learning to track at 100 fps with deep regression networks, European Conference on Computer Vision (2016), 749-765

      [6] Ma, Chao, et al, Hierarchical convolutional features for visual tracking, Proceedings of the IEEE International Conference on Computer Vision (2015), 3074-3082.




Article ID: 19594
DOI: 10.14419/ijet.v7i4.4.19594

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