  Convolutional neural network (CNN) models have demonstrated great success in
various computer vision tasks including image classification and object
detection. However, some equally important tasks such as visual tracking remain
relatively unexplored. We believe that a major hurdle that hinders the
application of CNN to visual tracking is the lack of properly labeled training
data. While existing applications that liberate the power of CNN often need an
enormous amount of training data in the order of millions, visual tracking
applications typically have only one labeled example in the first frame of each
video. We address this research issue here by pre-training a CNN offline and
then transferring the rich feature hierarchies learned to online tracking. The
CNN is also fine-tuned during online tracking to adapt to the appearance of the
tracked target specified in the first video frame. To fit the characteristics
of object tracking, we first pre-train the CNN to recognize what is an object,
and then propose to generate a probability map instead of producing a simple
class label. Using two challenging open benchmarks for performance evaluation,
our proposed tracker has demonstrated substantial improvement over other
state-of-the-art trackers.
