  One of the main challenges of visual object tracking comes from the arbitrary
appearance of objects. Most existing algorithms try to resolve this problem as
an object-specific task, i.e., the model is trained to regenerate or classify a
specific object. As a result, the model need to be initialized and retrained
for different objects. In this paper, we propose a more generic approach
utilizing a novel two-flow convolutional neural network (named YCNN). The YCNN
takes two inputs (one is object image patch, the other is search image patch),
then outputs a response map which predicts how likely the object appears in a
specific location. Unlike those object-specific approach, the YCNN is trained
to measure the similarity between two image patches. Thus it will not be
confined to any specific object. Furthermore the network can be end-to-end
trained to extract both shallow and deep convolutional features which are
dedicated for visual tracking. And once properly trained, the YCNN can be
applied to track all kinds of objects without further training and updating.
Benefiting from the once-for-all model, our algorithm is able to run at a very
high speed of 45 frames-per-second. The experiments on 51 sequences also show
that our algorithm achieves an outstanding performance.
