This toolkit is used to evaluate the tracker on the thermal infrared pedestrian tracking benchmark, PTB-TIR. Paper, Project
- [2019-11-4] We correct some annotation mistakes, so the results are slightly different from the results of the paper.
- [2019-11-4] We evaluate more trackers on the benchmark and provide their results in the raw results.
- You can download the overall dataset from Baidu Pan or Goggle Drive or Local.
- You can download the raw results of 27 trackers from Baidu Pan or Google Drive or Local.
- Download this toolkit and unzip it in your computer.
- Download and unzip the raw results and put it into the
results
folder of the toolkit. - Download and unzip the dataset and put it into the toolkit.
- Now, you can run
run_evaluation.m
andrun_speed.m
to draw the result plots. - You can configure
configTrackers.m
and then userun_tracker_interface.m
to run your own tracker on the benchmark.
- 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, 2018. [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]
- 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]
If you use this benchmark, please consider citing our paper.
@article{PTB-TIR,
title={PTB-TIR: A Thermal Infrared Pedestrian Tracking Benchmark},
author={Liu, Qiao and He, Zhenyu and Li, Xin and Zheng, Yuan},
journal={IEEE Transactions on Multimedia},
year={2019},
DOI ={10.1109/TMM.2019.2932615}
}
Feedbacks and comments are welcome! Feel free to contact us via liuqiao.hit@gmail.com or liuqiao@stu.hit.edu.cn