Abstract
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 video sequences.