  In this paper, we propose a self-supervised RGB-T tracking method. Different
from existing deep RGB-T trackers that use a large number of annotated RGB-T
image pairs for training, our RGB-T tracker is trained using unlabeled RGB-T
video pairs in a self-supervised manner. We propose a novel cross-input
consistency-based self-supervised training strategy based on the idea that
tracking can be performed using different inputs. Specifically, we construct
two distinct inputs using unlabeled RGB-T video pairs. We then track objects
using these two inputs to generate results, based on which we construct our
cross-input consistency loss. Meanwhile, we propose a reweighting strategy to
make our loss function robust to low-quality training samples. We build our
tracker on a Siamese correlation filter network. To the best of our knowledge,
our tracker is the first self-supervised RGB-T tracker. Extensive experiments
on two public RGB-T tracking benchmarks demonstrate that the proposed training
strategy is effective. Remarkably, despite training only with a corpus of
unlabeled RGB-T video pairs, our tracker outperforms seven supervised RGB-T
trackers on the GTOT dataset.

Computer Vision and Pattern Recognition;Artificial Intelligence;Multimedia