An Efficient Tracker via Multi-feature Adaptive Correlation Filter

Authors

Sixian Zhang, 1.State Key Laboratory for Strength and Vibration of Mechanical Structure, Xi'an Jiaotong University, Xi'an 710049, China;2.School of Aerospace, Xi'an Jiaotong University, Xi'an 710049, China;Follow
Yi Yang, 1.State Key Laboratory for Strength and Vibration of Mechanical Structure, Xi'an Jiaotong University, Xi'an 710049, China;2.School of Aerospace, Xi'an Jiaotong University, Xi'an 710049, China;Follow
Meng Zhang, 1.State Key Laboratory for Strength and Vibration of Mechanical Structure, Xi'an Jiaotong University, Xi'an 710049, China;2.School of Aerospace, Xi'an Jiaotong University, Xi'an 710049, China;
Pengbo Mi, 1.State Key Laboratory for Strength and Vibration of Mechanical Structure, Xi'an Jiaotong University, Xi'an 710049, China;2.School of Aerospace, Xi'an Jiaotong University, Xi'an 710049, China;

Abstract

Abstract: Aiming at the low tracking effect of the correlation filters tracker based on manual features in challenging scenes of rapid deformation and background clutter, a new correlation filter tracker based on Staple tracker is proposed. An appearance model based on HOG features and color-naming features is built to enhance the robustness to the challenging scenes of rapid deformation and background clutter. A self-adjust evaluation function is designed to merge the two kinds of feature information and a more discriminative feature is obtained. The novel online update strategies to reduce the training over-fitting and model drift for different features are proposed. The tracker shows excellent performance in accuracy and real-time capability on OTB2015 benchmark.

Recommended Citation

Zhang, Sixian; Yang, Yi; Zhang, Meng; and Mi, Pengbo (2022) "An Efficient Tracker via Multi-feature Adaptive Correlation Filter," Journal of System Simulation: Vol. 34: Iss. 8, Article 19.
DOI: 10.16182/j.issn1004731x.joss.21-0274
Available at: https://dc-china-simulation.researchcommons.org/journal/vol34/iss8/19

First Page

1864

Revised Date

2021-08-12

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.21-0274

Last Page

1873

CLC

TP391.4

Recommended Citation

Sixian Zhang, Yi Yang, Meng Zhang, Pengbo Mi. An Efficient Tracker via Multi-feature Adaptive Correlation Filter[J]. Journal of System Simulation, 2022, 34(8): 1864-1873.

Corresponding Author

Yi Yang,jiafeiyy@mail.xjtu.edu.cn

DOI

10.16182/j.issn1004731x.joss.21-0274