  In this study, we propose a novel RGB-T tracking framework by jointly
modeling both appearance and motion cues. First, to obtain a robust appearance
model, we develop a novel late fusion method to infer the fusion weight maps of
both RGB and thermal (T) modalities. The fusion weights are determined by using
offline-trained global and local multimodal fusion networks, and then adopted
to linearly combine the response maps of RGB and T modalities. Second, when the
appearance cue is unreliable, we comprehensively take motion cues, i.e., target
and camera motions, into account to make the tracker robust. We further propose
a tracker switcher to switch the appearance and motion trackers flexibly.
Numerous results on three recent RGB-T tracking datasets show that the proposed
tracker performs significantly better than other state-of-the-art algorithms.
