About LaSOT

Large-scale Single Object Tracking (LaSOT) aims to provide a dedicated platform for training data-hungry deep trackers as well as assessing long-term tracking performance. LaSOT is featured in

  • Large-scale: 1,550 sequences with more 3.87 millions frames
  • High-quality: Manual annotation with careful inspection in each frame
  • Category balance: 85 categories with each containing twenty (70 classes) or ten (15 classes) sequences
  • Long-term tracking: An average video length of around 2,500 frames (i.e., 83 seconds)
  • Comprehensive labeling: Providing both visual and lingual annotation for each sequence
  • Flexible Evaluation Protocol: Evaluation under three different protocols: no constraint, full-overlap and one-shot

Please check out the benchmark details and download links at LaSOT Benchmark page, evaluation toolkit and sample results at Evaluation and Result


  • 2020-09: Our LaSOT has been accepted to International Journal of Computer Vision.
  • 2020-09: A new subset with 15 new categories consisting of 150 challenging videos released. Check the dataset and paper.


Please consider citing the following papers if you use LaSOT for your research :)

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), 2020. (accepted)

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
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.


We appreciate questions and suggestions to Heng Fan at hefan@cs.stonybrook.edu or Haibin Ling at hling@cs.stonybrook.edu.