Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)

Improved Multi-sampling Kernelized Correlation Filter Target Tracking Algorithm

Authors
Ying Hou, Yemei He
Corresponding Author
Ying Hou
Available Online April 2019.
DOI
https://doi.org/10.2991/icmeit-19.2019.107How to use a DOI?
Keywords
Target tracking; Kernelized Correlation Filter (KCF); PSNR; Multi-Sampling.
Abstract
In order to solve the tracking failure of kernelized correlation filter (KCF) tracking algorithm in the case of target fast motion and motion blur, proposing a multi-sampling tracking algorithm based on KCF. Firstly, a PSNR-based judgment mechanism is introduced to determine whether the current frame target is tracking errors. If the tracking error occurs, the search range is extended to a muti-sampling search area. Finally re-detect the target of the current frame. The improved algorithm of this paper is compared with several classical correlation filter target tracking algorithms in the OTB video dataset. The experimental results show that the precision of this algorithm is 0.732 and the success rate is 0.575, ranking first, which is 5.3% and 4.3% higher than the KCF algorithm. Especially when the target has fast motion and motion blur, it has stronger tracking accuracy.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
Part of series
Advances in Computer Science Research
Publication Date
April 2019
ISBN
978-94-6252-708-9
ISSN
2352-538X
DOI
https://doi.org/10.2991/icmeit-19.2019.107How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Ying Hou
AU  - Yemei He
PY  - 2019/04
DA  - 2019/04
TI  - Improved Multi-sampling Kernelized Correlation Filter Target Tracking Algorithm
PB  - Atlantis Press
SP  - 671
EP  - 674
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmeit-19.2019.107
DO  - https://doi.org/10.2991/icmeit-19.2019.107
ID  - Hou2019/04
ER  -