  In this paper, we analyze the spatial information of deep features, and
propose two complementary regressions for robust visual tracking. First, we
propose a kernelized ridge regression model wherein the kernel value is defined
as the weighted sum of similarity scores of all pairs of patches between two
samples. We show that this model can be formulated as a neural network and thus
can be efficiently solved. Second, we propose a fully convolutional neural
network with spatially regularized kernels, through which the filter kernel
corresponding to each output channel is forced to focus on a specific region of
the target. Distance transform pooling is further exploited to determine the
effectiveness of each output channel of the convolution layer. The outputs from
the kernelized ridge regression model and the fully convolutional neural
network are combined to obtain the ultimate response. Experimental results on
two benchmark datasets validate the effectiveness of the proposed method.
