  Deep networks have been successfully applied to visual tracking by learning a
generic representation offline from numerous training images. However the
offline training is time-consuming and the learned generic representation may
be less discriminative for tracking specific objects. In this paper we present
that, even without offline training with a large amount of auxiliary data,
simple two-layer convolutional networks can be powerful enough to develop a
robust representation for visual tracking. In the first frame, we employ the
k-means algorithm to extract a set of normalized patches from the target region
as fixed filters, which integrate a series of adaptive contextual filters
surrounding the target to define a set of feature maps in the subsequent
frames. These maps measure similarities between each filter and the useful
local intensity patterns across the target, thereby encoding its local
structural information. Furthermore, all the maps form together a global
representation, which is built on mid-level features, thereby remaining close
to image-level information, and hence the inner geometric layout of the target
is also well preserved. A simple soft shrinkage method with an adaptive
threshold is employed to de-noise the global representation, resulting in a
robust sparse representation. The representation is updated via a simple and
effective online strategy, allowing it to robustly adapt to target appearance
variations. Our convolution networks have surprisingly lightweight structure,
yet perform favorably against several state-of-the-art methods on the CVPR2013
tracking benchmark dataset with 50 challenging videos.
