  We address the problem of multi-modal object tracking in video and explore
various options of fusing the complementary information conveyed by the visible
(RGB) and thermal infrared (TIR) modalities including pixel-level,
feature-level and decision-level fusion. Specifically, different from the
existing methods, paradigm of image fusion task is heeded for fusion at pixel
level. Feature-level fusion is fulfilled by attention mechanism with channels
excited optionally. Besides, at decision level, a novel fusion strategy is put
forward since an effortless averaging configuration has shown the superiority.
The effectiveness of the proposed decision-level fusion strategy owes to a
number of innovative contributions, including a dynamic weighting of the RGB
and TIR contributions and a linear template update operation. A variant of
which produced the winning tracker at the Visual Object Tracking Challenge 2020
(VOT-RGBT2020). The concurrent exploration of innovative pixel- and
feature-level fusion strategies highlights the advantages of the proposed
decision-level fusion method. Extensive experimental results on three
challenging datasets, \textit{i.e.}, GTOT, VOT-RGBT2019, and VOT-RGBT2020,
demonstrate the effectiveness and robustness of the proposed method, compared
to the state-of-the-art approaches. Code will be shared at
\textcolor{blue}{\emph{https://github.com/Zhangyong-Tang/DFAT}.
