Forward looking infrared (FLIR) imaging has been used in many areas of
research and everyday life, but it has been mostly employed in military
and security domains. In these fields, remote infrared target tracking
is a crucial element for surveillance. However, long-range captured IR
image sequences generally have poor contrast, variable illumination, and
high background clutter. These challenges make target tracking
difficult. This paper suggests a technique for target tracking in
different ranges in challenging FLIR image sequences, based on
Differential Kernel Covariance Descriptor (DKCD). This new method
diminishes rotation and illumination variation effects. The proposed
technique calculates the differential kernel matrix of reference target
by using various statistical and spatial features such as first and
second derivatives, location information, and the intensity value of
pixels. Later, the differential covariance matrix is constructed by
using different pixel features and applying the appropriate kernel
function to the matrix. Thanks to the kernel functions, the algorithm
redefines the target's differential spatial features in Hilbert space.
This process makes the descriptor non-linear. The predicted position of
the target is calculated with the nearest neighbor algorithm in the
candidate regions in the sub-frame. The performance of the suggested
single target tracking system is then tested on challenging real-life
This paper proposes a new nonlinear descriptor which mainly uses kernel
covariance matrix based on difference of features. The technique
minimizes the effect of pose variation, illumination, size, and
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This study received no specific financial support.
The authors declare that they have no competing interests.
The authors would like to thank O. Tuzel, F. Porikli, P. Meer, and D.
Comaniciu for rewarding discussions. The authors also thank the creators
of Linköping Thermal IR (LTIR) data set  and OTCBVS benchmark
dataset collection for sharing their real-