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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

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 Dubox 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 Dubox using the password: 2fad, 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 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

  • CMD-DiMP. Sun J, et al. Unsupervised Cross-Modal Distillation for Thermal Infrared Tracking, ACM MM, 2021. [Github]
  • MMNet. Liu Q, et al. Multi-task driven feature model for thermal infrared tracking, AAAI, 2020. [Github]
  • ECO-stir. Zhang L, et al. Synthetic data generation for end-to-end thermal infrared tracking, TIP, 2019. [Github]
  • MLSSNet. Liu Q, et al, Learning Deep Multi-Level Similarity for Thermal Infrared Object Tracking, TMM, 2020. [Github]
  • HSSNet. Li X, et al, Hierarchical spatial-aware Siamese network for thermal infrared object tracking, KBS, 2019.[Github]
  • MCFTS. Liu Q, et al, Deep convolutional neural networks for thermal infrared object tracking, KBS, 2017. [Github]

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 [email protected] or [email protected]