Home > Archive > 2021 > Volume 11 Number 1 (Jan. 2021) >
IJMLC 2021 Vol.11(1): 48-54 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2021.11.1.1013

Autonomous Tracking by an Adaptable Scaled KCF Algorithm

Din-Chang Tseng, Chien-Hung Chen, and Yi-Ming Chen

Abstract—A multicopter is equipped by a passive tracking device to follow a specified target. However, if want to track a non-controlled target, the passive tracking device is failed. We propose a vision-based tracking system for multicopters, used computer vision method to track any target without additional tracking devices. In this study, propose scale candidate graphs and scale tables to improve KCF. There are also stable results when the scale changes. The proposed an adaptable scaled KCF algorithm, when the KCF tracking failed, a feature-based matching detector is used to re-detect the target. Several experiments on various scene based on the proposed approach were conducted and evaluated. Stable tracking results were obtain to show the feasibility of the proposed system.

Index Terms—Computer vision, image processing, object tracking, multicopters.

The authors are with The Institute of Computer Science and Information Engineering, National Central University, Jhongli, 32001, Taiwan (e-mail: chienhung66@gmail.com).


Cite: Din-Chang Tseng, Chien-Hung Chen, and Yi-Ming Chen, "Autonomous Tracking by an Adaptable Scaled KCF Algorithm," International Journal of Machine Learning and Computing vol. 11, no. 1, pp. 48-54, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).


General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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