  We propose an online visual tracking algorithm by learning discriminative
saliency map using Convolutional Neural Network (CNN). Given a CNN pre-trained
on a large-scale image repository in offline, our algorithm takes outputs from
hidden layers of the network as feature descriptors since they show excellent
representation performance in various general visual recognition problems. The
features are used to learn discriminative target appearance models using an
online Support Vector Machine (SVM). In addition, we construct target-specific
saliency map by backpropagating CNN features with guidance of the SVM, and
obtain the final tracking result in each frame based on the appearance model
generatively constructed with the saliency map. Since the saliency map
visualizes spatial configuration of target effectively, it improves target
localization accuracy and enable us to achieve pixel-level target segmentation.
We verify the effectiveness of our tracking algorithm through extensive
experiment on a challenging benchmark, where our method illustrates outstanding
performance compared to the state-of-the-art tracking algorithms.
