  We present a fast and accurate visual tracking algorithm based on the
multi-domain convolutional neural network (MDNet). The proposed approach
accelerates feature extraction procedure and learns more discriminative models
for instance classification; it enhances representation quality of target and
background by maintaining a high resolution feature map with a large receptive
field per activation. We also introduce a novel loss term to differentiate
foreground instances across multiple domains and learn a more discriminative
embedding of target objects with similar semantics. The proposed techniques are
integrated into the pipeline of a well known CNN-based visual tracking
algorithm, MDNet. We accomplish approximately 25 times speed-up with almost
identical accuracy compared to MDNet. Our algorithm is evaluated in multiple
popular tracking benchmark datasets including OTB2015, UAV123, and TempleColor,
and outperforms the state-of-the-art real-time tracking methods consistently
even without dataset-specific parameter tuning.
