热红外行人跟踪基准matlab代码.zip

## PTB-TIR: A Thermal Infrared Pedestrian Tracking Benchmark (TMM19) This toolkit is used to evaluate the tracker on the thermal infrared pedestrian tracking benchmark, PTB-TIR. [Paper](https://arxiv.org/abs/1801.05944), [Project](https://sites.google.com/view/ptb-tir) ## News * [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. ## Download dataset and raw results * You can download the overall **dataset** from [Baidu Pan](https://pan.baidu.com/s/1RHhvc7fOA9QF5uHJ2LNdQQ) or [Goggle Drive](https://drive.google.com/open?id=1fWzsv-bluYMbntO0xFxDJ6K0WyTCNC7T) or [Local](http://www.hezhenyu.cn/UpLoadFiles/dataset/tirsequences_new.rar). * You can download the **raw results** of 30+ trackers from [Baidu Pan](https://pan.baidu.com/s/1bHqRdGyjEXsCQEEiF1OIJg?pwd=ssgb) or [Google Drive](https://drive.google.com/file/d/1NTE3u4LLU--ifi0lDgQ1_rksok0w5bG4/view?usp=sharing) or [Local](http://www.hezhenyu.cn/UpLoadFiles/dataset/results_OPE_all.rar). ## Usage 1. Download this toolkit and unzip it in your computer. 2. Download and unzip the raw results and put it into the `results` folder of the toolkit. 3. Download and unzip the dataset and put it into the toolkit. 4. Now, you can run `run_evaluation.m` and `run_speed.m` to draw the result plots. 5. You can configure `configTrackers.m` and then use `run_tracker_interface.m` to run your own tracker on the benchmark. ## Result's plots  ## Trackers and codes ### TIR trackers * **CMD-DiMP.** Sun J, et al. Unsupervised Cross-Modal Distillation for Thermal Infrared Tracking, ACM MM, 2021. [[Github]](https://github.com/zhanglichao/cmdTIRtracking) * **MMNet.** Liu Q, et al. Multi-task driven feature model for thermal infrared tracking, AAAI, 2020. [[Github]](https://github.com/QiaoLiuHit/MMNet) * **ECO-stir.** Zhang L, et al. Synthetic data generation for end-to-end thermal infrared tracking, TIP, 2018. [[Github]](https://github.com/zhanglichao/generatedTIR_tracking) * **MLSSNet.** Liu Q, et al, Learning Deep Multi-Level Similarity for Thermal Infrared Object Tracking, TMM, 2020. [[Github]](https://github.com/QiaoLiuHit/MLSSNet) * **HSSNet.** Li X, et al, Hierarchical spatial-aware Siamese network for thermal infrared object tracking, KBS, 2019.[[Github]](https://github.com/QiaoLiuHit/HSSNet) * **MCFTS.** Liu Q, et al, Deep convolutional neural networks for thermal infrared object tracking, KBS, 2017. [[Github]](https://github.com/QiaoLiuHit/MCFTS) ### RGB trackers * **ECO.** Danelljan M, et al, ECO: efficient convolution operators for tracking, CVPR, 2017. [[Github]](https://github.com/martin-danelljan/ECO) * **DeepSTRCF.** Li F et al, Learning spatial-temporal regularized correlation filters for visual tracking, CVPR, 2018. [[Github]](https://github.com/lifeng9472/STRCF) * **MDNet.** Nam H, et al, Learning multi-domain convolutional neural networks for visual tracking, CVPR, 2016. [[Github]](https://github.com/hyeonseobnam/MDNet) * **SRDCF.** Danelljan M, et al, Learning spatially regularized correlation filters for visual tracking, ICCV, 2015. [[Project]](https://www.cvl.isy.liu.se/research/objrec/visualtracking/regvistrack/) * **VITAL.** Song Y, et al., Vital: Visual tracking via adversarial learning, CVPR, 2018. [[Github]](https://github.com/ybsong00/Vital_release) * **TADT.** Li X, et al, Target-aware deep tracking, CVPR, 2019. [[Github]](https://github.com/XinLi-zn/TADT) * **MCCT.** Wang N, et al, Multi-cue correlation filters for robust visual tracking, CVPR, 2018. [[Github]](https://github.com/594422814/MCCT) * **Staple.** Bertinetto, L, et al, Staple: Complementary learners for real-time tracking, CVPR, 2016. [[Github]](https://github.com/bertinetto/staple) * **DSST.** Danelljan M, et al, Accurate scale estimation for robust visual tracking, BMVC, 2014. [[Github]](https://github.com/gnebehay/DSST) * **UDT.** Wang N, et al, Unsupervised deep tracking, CVPR, 2019. [[Github]](https://github.com/594422814/UDT) * **CREST.** Song Y, et al, Crest: Convolutional residual learning for visual tracking, ICCV, 2017. [[Github]](https://github.com/ybsong00/CREST-Release) * **SiamFC.** Bertinetto, L, et al, Fully-Convolutional Siamese Networks for Object Tracking, ECCVW, 2016. [[Github]](https://github.com/bertinetto/siamese-fc) * **SiamFC-tri.** Dong X, et al, Triplet loss in Siamese network for object tracking, ECCV, 2018. [[Github]](https://github.com/shenjianbing/TripletTracking) * **HDT.** Qi Y, et al, Hedged deep tracking, CVPR, 2016. [[Project]](https://sites.google.com/site/yuankiqi/hdt/) * **CFNet.** Valmadre, J, et al, End-to-end representation learning for correlation filter based tracking, CVPR, 2017. [[Github]](https://github.com/bertinetto/cfnet) * **HCF.** Ma, C, et al, Hierarchical convolutional features for visual tracking, ICCV, 2015. [[Github]](https://github.com/jbhuang0604/CF2) * **L1APG.** Bao, C, et al, Real time robust L1 tracker using accelerated proximal gradient approach, CVPR, 2012. [[Project]](http://www.dabi.temple.edu/~hbling/code_data.htm) * **SVM.** Wang N, et al, Understanding and diagnosing visual tracking systems, ICCV, 2015. [[Project]](http://winsty.net/tracker_diagnose.html) * **KCF.** Henriques, J, et al, High-speed tracking with kernelized correlation filters, TPAMI, 2015. [[Project]](http://www.robots.ox.ac.uk/~joao/circulant/) * **DSiam.** Guo, Q, et al, Learning dynamic siamese network for visual object tracking, ICCV, 2017. [[Github]](https://github.com/tsingqguo/DSiam) ## Citation 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} } ``` ## Contact Feedbacks and comments are welcome! Feel free to contact us via liuqiao.hit@gmail.com or liuqiao@stu.hit.edu.cn