  We propose a new context-aware correlation filter based tracking framework to
achieve both high computational speed and state-of-the-art performance among
real-time trackers. The major contribution to the high computational speed lies
in the proposed deep feature compression that is achieved by a context-aware
scheme utilizing multiple expert auto-encoders; a context in our framework
refers to the coarse category of the tracking target according to appearance
patterns. In the pre-training phase, one expert auto-encoder is trained per
category. In the tracking phase, the best expert auto-encoder is selected for a
given target, and only this auto-encoder is used. To achieve high tracking
performance with the compressed feature map, we introduce extrinsic denoising
processes and a new orthogonality loss term for pre-training and fine-tuning of
the expert auto-encoders. We validate the proposed context-aware framework
through a number of experiments, where our method achieves a comparable
performance to state-of-the-art trackers which cannot run in real-time, while
running at a significantly fast speed of over 100 fps.
