Open Access
ARTICLE
Robust Visual Tracking Models Designs Through Kernelized Correlation Filters
Detian Huang1, Peiting Gu2, Hsuan-Ming Feng3,*, Yanming Lin1, Lixin Zheng1
1 Fujian Provincial Academic Engineering Research Centre in Industrial Intellectual Techniques and Systems, College of Engineering, Huaqiao University, Quanzhou 362021, China
2 College of Mathematics and Computer Science, Quanzhou Normal University, Quanzhou 362000, China
3 Department of Computer Science and Information Engineering, National Quemoy University, No.1 University Rd, Kin-Ning Vallage Kinmen 892, Taiwan
* Corresponding Author: Hsuan-Ming Feng,
Intelligent Automation & Soft Computing 2020, 26(2), 313-322. https://doi.org/10.31209/2019.100000105
Abstract
To tackle the problem of illumination sensitive, scale variation, and occlusion in
the Kernelized Correlation Filters (KCF) tracker, an improved robust tracking
algorithm based on KCF is proposed. Firstly, the color attribute was introduced
to represent the target, and the dimension of target features was reduced
adaptively to obtain low-dimensional and illumination-insensitive target features
with the locally linear embedding approach. Secondly, an effective appearance
model updating strategy is designed, and then the appearance model can be
adaptively updated according to the Peak-to-Sidelobe Ratio value. Finally, the
low-dimensional color features and the HOG features are utilized to determine
the target state to further improve the robustness of the tracker. The
experimental results on OTB-2015 benchmark validate that the proposed
tracker can effectively solve the illumination variation, scale variation, partial
occlusion and deformation in the complex background.
Keywords
Cite This Article
APA Style
Huang, D., Gu, P., Feng, H., Lin, Y., Zheng, L. (2020). Robust visual tracking models designs through kernelized correlation filters. Intelligent Automation & Soft Computing, 26(2), 313-322. https://doi.org/10.31209/2019.100000105
Vancouver Style
Huang D, Gu P, Feng H, Lin Y, Zheng L. Robust visual tracking models designs through kernelized correlation filters. Intell Automat Soft Comput . 2020;26(2):313-322 https://doi.org/10.31209/2019.100000105
IEEE Style
D. Huang, P. Gu, H. Feng, Y. Lin, and L. Zheng "Robust Visual Tracking Models Designs Through Kernelized Correlation Filters," Intell. Automat. Soft Comput. , vol. 26, no. 2, pp. 313-322. 2020. https://doi.org/10.31209/2019.100000105