  RGB-Thermal (RGB-T) object tracking receives more and more attention due to
the strongly complementary benefits of thermal information to visible data.
However, RGB-T research is limited by lacking a comprehensive evaluation
platform. In this paper, we propose a large-scale video benchmark dataset for
RGB-T tracking.It has three major advantages over existing ones: 1) Its size is
sufficiently large for large-scale performance evaluation (total frame number:
234K, maximum frame per sequence: 8K). 2) The alignment between RGB-T sequence
pairs is highly accurate, which does not need pre- or post-processing. 3) The
occlusion levels are annotated for occlusion-sensitive performance analysis of
different tracking algorithms.Moreover, we propose a novel graph-based approach
to learn a robust object representation for RGB-T tracking. In particular, the
tracked object is represented with a graph with image patches as nodes. This
graph including graph structure, node weights and edge weights is dynamically
learned in a unified ADMM (alternating direction method of multipliers)-based
optimization framework, in which the modality weights are also incorporated for
adaptive fusion of multiple source data.Extensive experiments on the
large-scale dataset are executed to demonstrate the effectiveness of the
proposed tracker against other state-of-the-art tracking methods. We also
provide new insights and potential research directions to the field of RGB-T
object tracking.
