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 [email protected].