UPDATE: A new challenging subset is added!

We released a newly collected extension subset of 15 categories with 150 videos (very challenging!!!) for one-shot evaluation of tracking algorithms. Check the description in this paper. More details including the data, complete evaluation toolkit and results of 48 trackers are available at this project.

LaSOT_Evaluation_Toolkit

This toolkit is utilized for evaluating trackers' performance on a large-scale benchmark LaSOT (http://vision.cs.stonybrook.edu/~lasot/).

Notification (Downloading dataset and tracking results)

Please use the following links to download dataset (OneDrive is recommended):

Download LaSOT in the conference version

• Download the whole LaSOT in conference version through OneDriver: link or Google Drive: part-1 part-2 part-3

• Download each category in conference version through OneDriver: link

Download LaSOT-extension in the journal version

• Download the new extension in journal version through OneDriver: link or Google Drive: link
• Download each category of the new extension in journal version through OneDriver: link

In order to download the tracking results, please directly use the following link (including toolkit and complete results):

• Download the toolkit and complete tracking results: link (Google Drive)

Usage

• Download the repository, unzip it to your computer
• Download tracking result, unzip it to folder tracking_results/ (if this is not working, use the above link)
• Run run_tracker_performance_evaluation.m in Matlab

Notes

In the file run_tracker_performance_evaluation.m, you can

• change evaluation_dataset_type (line 25) for evaluation on all 1,400 sequences or 280 testing sequences
• change norm_dst (line 28) for precision or normalized precision plots

In the file utils/plot_draw_save.m

• change the plotting settings to get the appropriate plots

Citation

If you use LaSOT and this evaluation toolkit for you researches, please consider citing our paper:

• LaSOT: A High-quality Large-scale Single Object Tracking Benchmark
  H. Fan, H. Bai, L. Lin, F. Yang, P. Chu, G. Deng, S. Yu, Harshit, M. Huang, J Liu, Y. Xu, C. Liao, L Yuan, and H. Ling
  International Journal of Computer Vision (IJCV), 129: 439–461, 2021.
• LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking
  H. Fan, L. Lin, F. Yang, P. Chu, G. Deng, S. Yu, H. Bai, Y. Xu, C. Liao, and H. Ling
  In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.

Contact

If you have any questions on LaSOT, please feel free to contact Heng Fan at heng.fan@unt.edu.