@Article{2019.100000105,
AUTHOR = {Detian Huang, Peiting Gu, Hsuan-Ming Feng, Yanming Lin, Lixin Zheng},
TITLE = {Robust Visual Tracking Models Designs Through Kernelized Correlation  Filters},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {2},
PAGES = {313--322},
URL = {http://www.techscience.com/iasc/v26n2/39942},
ISSN = {2326-005X},
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.},
DOI = {10.31209/2019.100000105}
}