  RGBT tracking receives a surge of interest in the computer vision community,
but this research field lacks a large-scale and high-diversity benchmark
dataset, which is essential for both the training of deep RGBT trackers and the
comprehensive evaluation of RGBT tracking methods. To this end, we present a
Large-scale High-diversity benchmark for RGBT tracking (LasHeR) in this work.
LasHeR consists of 1224 visible and thermal infrared video pairs with more than
730K frame pairs in total. Each frame pair is spatially aligned and manually
annotated with a bounding box, making the dataset well and densely annotated.
LasHeR is highly diverse capturing from a broad range of object categories,
camera viewpoints, scene complexities and environmental factors across seasons,
weathers, day and night. We conduct a comprehensive performance evaluation of
12 RGBT tracking algorithms on the LasHeR dataset and present detailed analysis
to clarify the research room in RGBT tracking. In addition, we release the
unaligned version of LasHeR to attract the research interest for alignment-free
RGBT tracking, which is a more practical task in real-world applications. The
datasets and evaluation protocols are available at:
https://github.com/BUGPLEASEOUT/LasHeR.
