Domain adaptation (DA)-based training has demonstrated significant promise for obtaining
real-time nighttime unmanned aerial vehicle (UAV) trackers. However, the state-of-the-art …

Tracking objects from aerial imagery is significant in numerous remote sensing-based
applications, including environmental monitoring, security surveillance, and search & …

Multiobject tracking in unmanned aerial vehicle (UAV) scenes is a crucial task with
numerous applications across various domains. The goal of this task is to track multiple …

Due to the lack of discriminative features in infrared images, most of existing trackers cannot
separate a target from its background. There are some studies on generating discriminative …

Tracking arbitrary objects in aerial images presents formidable challenges to existing
trackers. Among these challenges, the large-scale variation and arbitrary geometry shape of …

Vanilla aerial trackers exhibit sensitivity to low-light conditions (eg, nighttime aerial tracking
scenario). To mitigate this, existing methods incorporate the light enhancement method as a …

Domain adaptation is an inspiring solution to the misalignment issue of day/night image
features for nighttime UAV tracking. However, the one-step adaptation paradigm is …

Nighttime unmanned aerial vehicle (UAV) tracking has been facilitated with indispensable
plug-and-play low-light enhancers. However, the introduction of low-light enhancers …

Recently, unmanned aerial vehicle (UAV) object tracking tasks have significantly improved
with the emergence of deep learning. However, owing to the object feature pollution caused …

Nighttime aerial tracking is an indispensable step towards around-the-clock real-world
applications. However, RGBbased tracking algorithms face significant challenges at night …