metadata

viewer: false
paperswithcode_id: lasot

Dataset Card for LaSOT

Dataset Description

Homepage: LaSOT homepage
Paper: LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking
Point of Contact: Heng Fan

Dataset Summary

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.

This repositoy contains the conference version of LaSOT, published on CVPR-19 (LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking).

LaSOT is featured in:

Large-scale: 1,400 sequences with more than 3.5 millions frames
High-quality: Manual annotation with careful inspection in each frame
Category balance: 70 categories, each containing 20 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

For the new subset (15 categories with 150 videos) in extended journal version (commonly referred to as LaSOT_ext), visit this repo.

Download

You can download the whole dataset using Git (with Git LFS):

git clone https://huggingface.co/datasets/l-lt/LaSOT

Alternatively, download the videos of a specific category manually from this page.

LaSOT is also distributed through serval cloud storage services:

In one zip file: OneDrive
One zip file per category: OneDrive or Baidu Pan

Unzip

Unzip all zip files and the paths should be organized as following:

├── airplane
│   ├── airplane-1
│   ...
├── basketball
...
├── training_set.txt
└── testing_set.txt

Evaluation Metrics and Toolkit

See the homepage for more information.