  With the popularity of multi-modal sensors, visible-thermal (RGB-T) object
tracking is to achieve robust performance and wider application scenarios with
the guidance of objects' temperature information. However, the lack of paired
training samples is the main bottleneck for unlocking the power of RGB-T
tracking. Since it is laborious to collect high-quality RGB-T sequences, recent
benchmarks only provide test sequences. In this paper, we construct a
large-scale benchmark with high diversity for visible-thermal UAV tracking
(VTUAV), including 500 sequences with 1.7 million high-resolution (1920
$\times$ 1080 pixels) frame pairs. In addition, comprehensive applications
(short-term tracking, long-term tracking and segmentation mask prediction) with
diverse categories and scenes are considered for exhaustive evaluation.
Moreover, we provide a coarse-to-fine attribute annotation, where frame-level
attributes are provided to exploit the potential of challenge-specific
trackers. In addition, we design a new RGB-T baseline, named Hierarchical
Multi-modal Fusion Tracker (HMFT), which fuses RGB-T data in various levels.
Numerous experiments on several datasets are conducted to reveal the
effectiveness of HMFT and the complement of different fusion types. The project
is available at here.
