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

Update

  • 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.

Reference

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.

Contact

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