  In this paper, we present a Large-Scale and high-diversity general Thermal
InfraRed (TIR) Object Tracking Benchmark, called LSOTBTIR, which consists of an
evaluation dataset and a training dataset with a total of 1,400 TIR sequences
and more than 600K frames. We annotate the bounding box of objects in every
frame of all sequences and generate over 730K bounding boxes in total. To the
best of our knowledge, LSOTB-TIR is the largest and most diverse TIR object
tracking benchmark to date. To evaluate a tracker on different attributes, we
define 4 scenario attributes and 12 challenge attributes in the evaluation
dataset. By releasing LSOTB-TIR, we encourage the community to develop deep
learning based TIR trackers and evaluate them fairly and comprehensively. We
evaluate and analyze more than 30 trackers on LSOTB-TIR to provide a series of
baselines, and the results show that deep trackers achieve promising
performance. Furthermore, we re-train several representative deep trackers on
LSOTB-TIR, and their results demonstrate that the proposed training dataset
significantly improves the performance of deep TIR trackers. Codes and dataset
are available at https://github.com/QiaoLiuHit/LSOTB-TIR.
