This toolkit is utilized for evaluating trackers' performance on a large-scale benchmark LaSOT (https://cis.temple.edu/lasot/).
There is a problem with the data sever. Please use the following links to download dataset:
- Download the whole dataset through Google driver: https://bit.ly/LaSOTAll
- Download each category through Google driver: https://bit.ly/LaSOTEach
- Download the whole dataset through Baidu Pan: https://pan.baidu.com/s/1UbcQIU-Fpps7Jqq4WHRRkA
- Download each category through Baidu Pan: https://pan.baidu.com/s/1xFANiqkBHytE7stMOLUpLQ
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: https://cis.temple.edu/lasot/toolkit/LaSOT_Evaluation_Toolkit.zip
- 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.min Matlab
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
If you use LaSOT and this evaluation toolkit for you researches, please consider citing our paper:
- H. Fan*, L. Lin*, F. Yang*, P. Chu*, G. Deng, S. Yu, H. Bai, Y. Xu, C. Liao, and H. Ling. LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking. In CVPR, 2019.
If you have any questions on LaSOT, please feel free to contain Heng Fan at hengfan@temple.