  In this paper, we study a discriminatively trained deep convolutional network
for the task of visual tracking. Our tracker utilizes both motion and
appearance features that are extracted from a pre-trained dual stream deep
convolution network. We show that the features extracted from our dual-stream
network can provide rich information about the target and this leads to
competitive performance against state of the art tracking methods on a visual
tracking benchmark.
