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Dataset Card for LaSOT

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 repository contains the conference version of LaSOT, published in 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 LaSOText), visit this repo.

Download

You can download the whole dataset via the huggingface_hub library (guide):

from huggingface_hub import snapshot_download
snapshot_download(repo_id='l-lt/LaSOT', repo_type='dataset', local_dir='/path/to/download')

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

LaSOT is also distributed through several cloud storage services (currently only OneDrive):

Setup

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

β”œβ”€β”€ airplane
β”‚   β”œβ”€β”€ airplane-1
β”‚   ...
β”œβ”€β”€ basketball
...
β”œβ”€β”€ training_set.txt
└── testing_set.txt

Evaluation Metrics and Toolkit

See the homepage for more information.

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