  After observing that the features used in most online discriminatively
trained trackers are not optimal, in this paper, we propose a novel and
effective architecture to learn optimal feature embeddings for online
discriminative tracking. Our method, called DCFST, integrates the solver of a
discriminant model that is differentiable and has a closed-form solution into
convolutional neural networks. Then, the resulting network can be trained in an
end-to-end way, obtaining optimal feature embeddings for the discriminant
model-based tracker. As an instance, we apply the popular ridge regression
model in this work to demonstrate the power of DCFST. Extensive experiments on
six public benchmarks, OTB2015, NFS, GOT10k, TrackingNet, VOT2018, and VOT2019,
show that our approach is efficient and generalizes well to class-agnostic
target objects in online tracking, thus achieves state-of-the-art accuracy,
while running beyond the real-time speed. Code will be made available.
