  In recent years, two types of trackers, namely correlation filter based
tracker (CF tracker) and structured output tracker (Struck), have exhibited the
state-of-the-art performance. However, there seems to be lack of analytic work
on their relations in the computer vision community. In this paper, we
investigate two state-of-the-art CF trackers, i.e., spatial regularization
discriminative correlation filter (SRDCF) and correlation filter with limited
boundaries (CFLB), and Struck, and reveal their relations. Specifically, after
extending the CFLB to its multiple channel version we prove the relation
between SRDCF and CFLB on the condition that the spatial regularization factor
of SRDCF is replaced by the masking matrix of CFLB. We also prove the
asymptotical approximate relation between SRDCF and Struck on the conditions
that the spatial regularization factor of SRDCF is replaced by an indicator
function of object bounding box, the weights of SRDCF in its loss item are
replaced by those of Struck, the linear kernel is employed by Struck, and the
search region tends to infinity. Extensive experiments on public benchmarks
OTB50 and OTB100 are conducted to verify our theoretical results. Moreover, we
explain how detailed differences among SRDCF, CFLB, and Struck would give rise
to slightly different performances on visual sequences
