Improved Multi-sampling Kernelized Correlation Filter Target Tracking Algorithm

DOI

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.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Ying Hou
AU  - Yemei He
PY  - 2019/04
DA  - 2019/04
TI  - Improved Multi-sampling Kernelized Correlation Filter Target Tracking Algorithm
BT  - Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)
PB  - Atlantis Press
SP  - 671
EP  - 674
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmeit-19.2019.107
DO  - 10.2991/icmeit-19.2019.107
ID  - Hou2019/04
ER  -