Detian Huang1, Peiting Gu2, Hsuan-Ming Feng3,*, Yanming Lin1, Lixin Zheng1
Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 313-322, 2020, DOI: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 More >
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