  Traditional framework of discriminative correlation filters (DCF) is often
subject to undesired boundary effects. Several approaches to enlarge search
regions have been already proposed in the past years to make up for this
shortcoming. However, with excessive background information, more background
noises are also introduced and the discriminative filter is prone to learn from
the ambiance rather than the object. This situation, along with appearance
changes of objects caused by full/partial occlusion, illumination variation,
and other reasons has made it more likely to have aberrances in the detection
process, which could substantially degrade the credibility of its result.
Therefore, in this work, a novel approach to repress the aberrances happening
during the detection process is proposed, i.e., aberrance repressed correlation
filter (ARCF). By enforcing restriction to the rate of alteration in response
maps generated in the detection phase, the ARCF tracker can evidently suppress
aberrances and is thus more robust and accurate to track objects. Considerable
experiments are conducted on different UAV datasets to perform object tracking
from an aerial view, i.e., UAV123, UAVDT, and DTB70, with 243 challenging image
sequences containing over 90K frames to verify the performance of the ARCF
tracker and it has proven itself to have outperformed other 20 state-of-the-art
trackers based on DCF and deep-based frameworks with sufficient speed for
real-time applications.
