  Recently, discriminatively learned correlation filters (DCF) has drawn much
attention in visual object tracking community. The success of DCF is
potentially attributed to the fact that a large amount of samples are utilized
to train the ridge regression model and predict the location of object. To
solve the regression problem in an efficient way, these samples are all
generated by circularly shifting from a search patch. However, these synthetic
samples also induce some negative effects which weaken the robustness of DCF
based trackers.
  In this paper, we propose a Convolutional Regression framework for visual
tracking (CRT). Instead of learning the linear regression model in a closed
form, we try to solve the regression problem by optimizing a one-channel-output
convolution layer with Gradient Descent (GD). In particular, the receptive
field size of the convolution layer is set to the size of object. Contrary to
DCF, it is possible to incorporate all "real" samples clipped from the whole
image. A critical issue of the GD approach is that most of the convolutional
samples are negative and the contribution of positive samples will be
suppressed. To address this problem, we propose a novel Automatic Hard Negative
Mining method to eliminate easy negatives and enhance positives. Extensive
experiments are conducted on a widely-used benchmark with 100 sequences. The
results show that the proposed algorithm achieves outstanding performance and
outperforms almost all the existing DCF based algorithms.
