README.md

June 22, 2026 ยท View on GitHub

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

This toolkit is used to evaluate general thermal infrared (TIR) trackers on the TIR object tracking benchmark, LSOTB-TIR, which consists of a large-scale training dataset and an evaluation dataset with a total of 1,400 TIR image sequences and more than 600K frames. To evaluate a TIR 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. Paper, Supplementary materials Alt text

License and Usage

This repository contains both source code and dataset-related materials. The source code, scripts, and software tools in this repository are released under the MIT License, as specified in the LICENSE file. However, the LSOTB-TIR dataset, including but not limited to thermal infrared images, annotations, metadata, benchmark splits, and related data files, is not covered by the MIT License. The LSOTB-TIR dataset is provided free of charge for academic research and non-commercial use only. Any commercial use, including but not limited to training, fine-tuning, evaluating, or developing object tracking or computer vision models for commercial products or services, requires prior written authorization from the dataset owners. For commercial licensing inquiries, please contact: Dr. Liu Email: liuqiao.hit@gmail.com

News

  • 2020-08, Our paper is accepted by ACM Multimedia Conference 2020.
  • 2020-11, We update the evaluation dataset because we miss a test sequence 'cat_D_001'.
  • 2022-10, We provide a 'LSOTB-TIR.json' file at here Baidu or TeraBox for evaluating on the pysot toolkit.
  • 2023-01, Our extended paper is accepted by TNNLS.paper, evaluation dataset

Characteristics

  • Large-scale: 1400 TIR sequences, 600K+ frames, 730K+ bounding boxes.
  • High-diversity: 12 challenges, 4 scenario, 47 object classes.
  • Contain both training and evaluation data sets.
  • Provide 30+ tracker's evaluation results.
  • Provide short-term and long-term TIR tracking evaluation.

Download dataset and evaluation results

  • Download the dataset and 30+ tracker's evaluation raw results from TeraBox, if you are not in china.
  • Download the dataset and 30+ tracker's evaluation raw results from Baidu Pan using the password: dr3i, if you are in china.
  • Download the evaluation dataset of the TNNLS version from Baidu Pan or TeraBox and corresponding raw results from here.

Usage

  1. Download the evaluation dataset and put it into the sequences folder.
  2. Download the evaluation raw results and put them into the results folder.
  3. Run run_evaluation.m and run_speed.m to draw the result plots.
  4. Configure configTrackers.m and then use main_running_one.m to run your own tracker on the benchmark.

Result's plots

Alt text

Trackers and codes

TIR trackers

  • DDAT. Liu Q, et al. Unsupervised Domain Adaptive Thermal Infrared Tracking, TMM, 2026. [Code]
  • ViT-TIR. Ma S, et al. Vision-Inspired Transformer-Based Thermal Infrared Target Tracking Framework for Internet of Things, IoTJ, 2026.
  • ASMTrack. Ma S, et al. ASMTrack: Thermal Infrared Target Tracking Network Based on Atkinson-Shiffrin Memory Model, TITS, 2025.
  • FFTR. Liao D, et al. Fine-Grained Feature and Template Reconstruction for TIR Object Tracking, IEEE TCSVT, 2025.
  • MGTrack. Ma S, et al. Transformer-Based Memory Guided Thermal Infrared Target Tracking Framework for Traffic Assistance, TITS, 2025.
  • SGSiamAttn. Li X, et al. Saliency Guided Siamese Attention Network for Infrared Ship Target Tracking, TIV, 2025.
  • EHDA. Li Q, et al. Efficient Hierarchical Domain Adaptive Thermal Infrared Tracking, ICASSP, 2025.
  • ReFocus. Lai S, et al. Refocus the Attention for Parameter-Efficient Thermal Infrared Object Tracking, TNNLS, 2025.[Code]
  • MGAM. Gao P, et al. Learning multi-level graph attentional representation for thermal infrared object tracking, EAAI, 2025.
  • NLMTrack. Yan M, et al. Coordinate-aware thermal infrared tracking via natural language modeling, ESWA, 2025.[Code]
  • LDFG. Yang C, et al. Learning diverse fine-grained features for thermal infrared tracking, ESWA, 2024.
  • STFNet. Qi M, et al. Exploring reliable infrared object tracking with spatio-temporal fusion transformer, KBS, 2024.
  • ASTM. Yuan D, et al. Aligned Spatial-Temporal Memory Network for Thermal Infrared Target Tracking, TCSII, 2023.
  • RMCM. Zha Y, et al. Reversible Modal Conversion Model for Thermal Infrared Tracking, IEEE MultiMedia, 2023.
  • CMCD. Li H, et al. Efficient thermal infrared tracking with cross-modal compress distillation, EAAI, 2023.
  • CMD-DiMP. Sun J, et al. Unsupervised Cross-Modal Distillation for Thermal Infrared Tracking, ACM MM, 2021. [Code]
  • MMNet. Liu Q, et al. Multi-task driven feature model for thermal infrared tracking, AAAI, 2020. [Code]
  • ECO-stir. Zhang L, et al. Synthetic data generation for end-to-end thermal infrared tracking, TIP, 2019. [Code]
  • MLSSNet. Liu Q, et al, Learning Deep Multi-Level Similarity for Thermal Infrared Object Tracking, TMM, 2020. [Code]
  • HSSNet. Li X, et al, Hierarchical spatial-aware Siamese network for thermal infrared object tracking, KBS, 2019.[Code]
  • MCFTS. Liu Q, et al, Deep convolutional neural networks for thermal infrared object tracking, KBS, 2017. [Code]

RGB trackers

  • ECO. Danelljan M, et al, ECO: efficient convolution operators for tracking, CVPR, 2017. [Github]
  • DeepSTRCF. Li F et al, Learning spatial-temporal regularized correlation filters for visual tracking, CVPR, 2018. [Github]
  • MDNet. Nam H, et al, Learning multi-domain convolutional neural networks for visual tracking, CVPR, 2016. [Github]
  • SRDCF. Danelljan M, et al, Learning spatially regularized correlation filters for visual tracking, ICCV, 2015. [Project]
  • VITAL. Song Y, et al., Vital: Visual tracking via adversarial learning, CVPR, 2018. [Github]
  • TADT. Li X, et al, Target-aware deep tracking, CVPR, 2019. [Github]
  • MCCT. Wang N, et al, Multi-cue correlation filters for robust visual tracking, CVPR, 2018. [Github]
  • Staple. Bertinetto, L, et al, Staple: Complementary learners for real-time tracking, CVPR, 2016. [Github]
  • DSST. Danelljan M, et al, Accurate scale estimation for robust visual tracking, BMVC, 2014. [Github]
  • UDT. Wang N, et al, Unsupervised deep tracking, CVPR, 2019. [Github]
  • CREST. Song Y, et al, Crest: Convolutional residual learning for visual tracking, ICCV, 2017. [Github]
  • SiamFC. Bertinetto, L, et al, Fully-Convolutional Siamese Networks for Object Tracking, ECCVW, 2016. [Github]
  • SiamFC-tri. Dong X, et al, Triplet loss in Siamese network for object tracking, ECCV, 2018. [Github]
  • HDT. Qi Y, et al, Hedged deep tracking, CVPR, 2016. [Project]
  • CFNet. Valmadre, J, et al, End-to-end representation learning for correlation filter based tracking, CVPR, 2017. [Github]
  • HCF. Ma, C, et al, Hierarchical convolutional features for visual tracking, ICCV, 2015. [Github]
  • L1APG. Bao, C, et al, Real time robust L1 tracker using accelerated proximal gradient approach, CVPR, 2012. [Project]
  • SVM. Wang N, et al, Understanding and diagnosing visual tracking systems, ICCV, 2015. [Project]
  • KCF. Henriques, J, et al, High-speed tracking with kernelized correlation filters, TPAMI, 2015. [Project]
  • DSiam. Guo, Q, et al, Learning dynamic siamese network for visual object tracking, ICCV, 2017. [Github]

Contact

Feedbacks and comments are welcome! Feel free to contact us via liuqiao.hit@gmail.com or liuqiao@stu.hit.edu.cn