  Aerial tracking, which has exhibited its omnipresent dedication and splendid
performance, is one of the most active applications in the remote sensing
field. Especially, unmanned aerial vehicle (UAV)-based remote sensing system,
equipped with a visual tracking approach, has been widely used in aviation,
navigation, agriculture,transportation, and public security, etc. As is
mentioned above, the UAV-based aerial tracking platform has been gradually
developed from research to practical application stage, reaching one of the
main aerial remote sensing technologies in the future. However, due to the
real-world onerous situations, e.g., harsh external challenges, the vibration
of the UAV mechanical structure (especially under strong wind conditions), the
maneuvering flight in complex environment, and the limited computation
resources onboard, accuracy, robustness, and high efficiency are all crucial
for the onboard tracking methods. Recently, the discriminative correlation
filter (DCF)-based trackers have stood out for their high computational
efficiency and appealing robustness on a single CPU, and have flourished in the
UAV visual tracking community. In this work, the basic framework of the
DCF-based trackers is firstly generalized, based on which, 23 state-of-the-art
DCF-based trackers are orderly summarized according to their innovations for
solving various issues. Besides, exhaustive and quantitative experiments have
been extended on various prevailing UAV tracking benchmarks, i.e., UAV123,
UAV123@10fps, UAV20L, UAVDT, DTB70, and VisDrone2019-SOT, which contain 371,903
frames in total. The experiments show the performance, verify the feasibility,
and demonstrate the current challenges of DCF-based trackers onboard UAV
tracking.
