  Convolutional neural networks (CNN) based tracking approaches have shown
favorable performance in recent benchmarks. Nonetheless, the chosen CNN
features are always pre-trained in different tasks and individual components in
tracking systems are learned separately, thus the achieved tracking performance
may be suboptimal. Besides, most of these trackers are not designed towards
real-time applications because of their time-consuming feature extraction and
complex optimization details. In this paper, we propose an end-to-end framework
to learn the convolutional features and perform the tracking process
simultaneously, namely, a unified convolutional tracker (UCT). Specifically,
the UCT treats feature extractor and tracking process both as convolution
operation and trains them jointly, which enables learned CNN features are
tightly coupled with tracking process. During online tracking, an efficient
model updating method is proposed by introducing peak-versus-noise ratio (PNR)
criterion, and scale changes are handled efficiently by incorporating a scale
branch into network. Experiments are performed on four challenging tracking
datasets: OTB2013, OTB2015, VOT2015 and VOT2016. Our method achieves leading
performance on these benchmarks while maintaining beyond real-time speed.
