  Discriminative correlation filters (DCFs) have been shown to perform
superiorly in visual tracking. They only need a small set of training samples
from the initial frame to generate an appearance model. However, existing DCFs
learn the filters separately from feature extraction, and update these filters
using a moving average operation with an empirical weight. These DCF trackers
hardly benefit from the end-to-end training. In this paper, we propose the
CREST algorithm to reformulate DCFs as a one-layer convolutional neural
network. Our method integrates feature extraction, response map generation as
well as model update into the neural networks for an end-to-end training. To
reduce model degradation during online update, we apply residual learning to
take appearance changes into account. Extensive experiments on the benchmark
datasets demonstrate that our CREST tracker performs favorably against
state-of-the-art trackers.
