  Unmanned aerial vehicle (UAV) tracking has wide potential applications in
such as agriculture, navigation, and public security. However, the limitations
of computing resources, battery capacity, and maximum load of UAV hinder the
deployment of deep learning-based tracking algorithms on UAV. Consequently,
discriminative correlation filters (DCF) trackers stand out in the UAV tracking
community because of their high efficiency. However, their precision is usually
much lower than trackers based on deep learning. Model compression is a
promising way to narrow the gap (i.e., effciency, precision) between DCF- and
deep learning- based trackers, which has not caught much attention in UAV
tracking. In this paper, we propose the P-SiamFC++ tracker, which is the first
to use rank-based filter pruning to compress the SiamFC++ model, achieving a
remarkable balance between efficiency and precision. Our method is general and
may encourage further studies on UAV tracking with model compression. Extensive
experiments on four UAV benchmarks, including UAV123@10fps, DTB70, UAVDT and
Vistrone2018, show that P-SiamFC++ tracker significantly outperforms
state-of-the-art UAV tracking methods.
