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.
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)
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: http://vision.cs.stonybrook.edu/~lasot/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)
In the file
run_tracker_performance_evaluation.m, you can
evaluation_dataset_type(line 25) for evaluation on all 1,400 sequences or 280 testing sequences
norm_dst(line 28) for precision or normalized precision plots
In the file
- 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:
- 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
- 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.