Learning Feature Embeddings for Discriminant Model based Tracking

Understand

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.