Abstract
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 autoencoders. 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 signifi-
cantly fast speed of over 100 fps.