  Discriminative correlation filters (DCF) have recently shown excellent
performance in visual object tracking area. In this paper, we summarize the
methods of updating model filter from discriminative correlation filter (DCF)
based tracking algorithms and analyzes similarities and differences among these
methods. We deduce the relationship between updating coefficient in high
dimension (kernel trick), updating filter in frequency domain and updating
filter in spatial domain, and analyze the difference among these different
ways. We also analyze the difference between the updating filter directly and
updating filter's numerator (object response power) with updating filter's
denominator (filter's power). The experiments about comparing different
updating methods and visualizing the template filters are used to prove our
derivation.
