Siamese network-based trackers have shown remarkable success in aerial
tracking. Most previous works, however, usually perform template matching only
between the initial template and the search region and thus fail to deal with
rapidly changing targets that often appear in aerial tracking. As a remedy,
this work presents Building Appearance Collection Tracking (BACTrack). This
simple yet effective tracking framework builds a dynamic collection of target
templates online and performs efficient multi-template matching to achieve
robust tracking. Specifically, BACTrack mainly comprises a Mixed-Temporal
Transformer (MTT) and an appearance discriminator. The former is responsible
for efficiently building relationships between the search region and multiple
target templates in parallel through a mixed-temporal attention mechanism. At
the same time, the appearance discriminator employs an online adaptive
template-update strategy to ensure that the collected multiple templates remain
reliable and diverse, allowing them to closely follow rapid changes in the
target's appearance and suppress background interference during tracking.
Extensive experiments show that our BACTrack achieves top performance on four
challenging aerial tracking benchmarks while maintaining an impressive speed of
over 87 FPS on a single GPU. Speed tests on embedded platforms also validate
our potential suitability for deployment on UAV platforms.

Siamese network-based trackers have been successful in aerial tracking, but this work presents BACTrack, a tracking framework that builds a dynamic collection of target templates online and performs efficient multi-template matching, achieving robust tracking with top performance on aerial tracking benchmarks and high speed on GPU and embedded platforms.