LSOTB-TIR: A Large-Scale High-Diversity Thermal Infrared Object Tracking Benchmark

Published in Proceedings of the 28th ACM International Conference on Multimedia (MM ’20), 2020

In this paper, we present a Large-Scale and high-diversity general Thermal InfraRed (TIR) Object Tracking Benchmark, called LSOTB-TIR, 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.

Download paper here and Code and Dataset


If you use the code or dataset, please consider citing our paper.

  title={LSOTB-TIR: A Large-Scale High-Diversity Thermal Infrared Object Tracking Benchmark},
  author={Liu, Qiao and Li, Xin and He, Zhenyu and Li, Chenglong and Li, Jun and Zhou, Zikun and Yuan, Di and Li, Jing and Yang, Kai and Fan, Nana and others},
  booktitle={Proceedings of the 28th ACM International Conference on Multimedia},