  Correlation filter-based tracking has been widely applied in unmanned aerial
vehicle (UAV) with high efficiency. However, it has two imperfections, i.e.,
boundary effect and filter corruption. Several methods enlarging the search
area can mitigate boundary effect, yet introducing undesired background
distraction. Existing frame-by-frame context learning strategies for repressing
background distraction nevertheless lower the tracking speed. Inspired by
keyframe-based simultaneous localization and mapping, keyfilter is proposed in
visual tracking for the first time, in order to handle the above issues
efficiently and effectively. Keyfilters generated by periodically selected
keyframes learn the context intermittently and are used to restrain the
learning of filters, so that 1) context awareness can be transmitted to all the
filters via keyfilter restriction, and 2) filter corruption can be repressed.
Compared to the state-of-the-art results, our tracker performs better on two
challenging benchmarks, with enough speed for UAV real-time applications.
