  Correlation filters (CF) have received considerable attention in visual
tracking because of their computational efficiency. Leveraging deep features
via off-the-shelf CNN models (e.g., VGG), CF trackers achieve state-of-the-art
performance while consuming a large number of computing resources. This limits
deep CF trackers to be deployed to many mobile platforms on which only a
single-core CPU is available. In this paper, we propose to jointly compress and
transfer off-the-shelf CNN models within a knowledge distillation framework. We
formulate a CNN model pretrained from the image classification task as a
teacher network, and distill this teacher network into a lightweight student
network as the feature extractor to speed up CF trackers. In the distillation
process, we propose a fidelity loss to enable the student network to maintain
the representation capability of the teacher network. Meanwhile, we design a
tracking loss to adapt the objective of the student network from object
recognition to visual tracking. The distillation process is performed offline
on multiple layers and adaptively updates the student network using a
background-aware online learning scheme. Extensive experiments on five
challenging datasets demonstrate that the lightweight student network
accelerates the speed of state-of-the-art deep CF trackers to real-time on a
single-core CPU while maintaining almost the same tracking accuracy.
