Abstract: Aiming at addressing the problem that traditional kernel correlation filter (KCF) algorithm
often fails in occlusion cases, we proposed an adaptive kernel correlation filter algorithm which can
enhance the target edge. First we employ kernel correlation filter to find the location of target. Then
we enhance the edge of location area in order to make the target characteristics more outstanding and
improve the classifier performance. At last, we calculate the response of the target location intensity
vector to change the template parameters adaptively. In experiments, we selected 15 sets of
challenging public video sequences to test the effectiveness of our method. compared with the KCF
algorithm which is best in existing correlation filters, the center position error of our method
reduced 9.6 pixels, the average success rate increased by 7.55% and the average distance precision
increased by 21.62%. The experiment’s results show that the proposed algorithm has strong
adaptability in obscured, rotating and rapid complex conditions. Furthermore, it has important theory
research and application value.

Key words: correlation filter, visual tracking, adaptive tracking