
# Dataset Card for LaSOT

## Dataset Description

- **Homepage:** [LaSOT homepage](http://vision.cs.stonybrook.edu/~lasot/)
- **Paper:** [LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking](https://arxiv.org/abs/1809.07845)
- **Point of Contact:** [Heng Fan](heng.fan@unt.edu)

### Dataset Summary

**La**rge-scale **S**ingle **O**bject **T**racking (**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](https://arxiv.org/abs/1809.07845)).

**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](https://arxiv.org/abs/2009.03465) (commonly referred to as LaSOT<sub>ext</sub>), visit this [repo](https://huggingface.co/datasets/l-lt/LaSOT-ext).


## Download

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

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

Alternatively, download the videos of a specific category manually from this [page](https://huggingface.co/datasets/l-lt/LaSOT/tree/main).

LaSOT is also distributed through serval cloud storage services:

* In one zip file: [OneDrive](https://1drv.ms/u/s!Akt_zO4y_u6DgoQsxl9ixr5Y393qWA?e=7yTwjc)

* One zip file per category: [OneDrive](https://1drv.ms/f/s!Akt_zO4y_u6DgoNSoMJrfnVwveDjhA?e=PBeyuD) or [Baidu Pan](https://pan.baidu.com/s/1xFANiqkBHytE7stMOLUpLQ)

### 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](http://vision.cs.stonybrook.edu/~lasot/results.html) for more information.
arxiv:1809.07845 arxiv:2009.03465 region:us
{"paperswithcode_id": "lasot", "viewer": false}
TAGS
#arxiv-1809.07845 #arxiv-2009.03465 #region-us 


# 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<sub>ext</sub>), visit this repo.


## Download

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



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:


## Evaluation Metrics and Toolkit

See the homepage for more information.
# 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<sub>ext</sub>), visit this repo. ## Download

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



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: ## Evaluation Metrics and Toolkit

See the homepage for more information.
TAGS
#arxiv-1809.07845 #arxiv-2009.03465 #region-us 
 # 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<sub>ext</sub>), visit this repo. ## Download

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



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: ## Evaluation Metrics and Toolkit

See the homepage for more information.
passage: TAGS
#arxiv-1809.07845 #arxiv-2009.03465 #region-us 
# 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<sub>ext</sub>), visit this repo.## Download

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



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:## Evaluation Metrics and Toolkit

See the homepage for more information.