
# Dataset Card for LaSOT-ext

## Dataset Description

- **Homepage:** [LaSOT homepage](http://vision.cs.stonybrook.edu/~lasot/)
- **Paper:** [LaSOT: A High-quality Large-scale Single Object Tracking Benchmark](https://arxiv.org/abs/2009.03465)
- **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 new subset provided in the journal version of LaSOT (commonly called **LaSOT<sub>ext</sub>**), published on IJCV ([LaSOT: A High-quality Large-scale Single Object Tracking Benchmark](https://arxiv.org/abs/2009.03465)).

For the training/testing splits of LaSOT (conference version), visit this [repo](https://huggingface.co/datasets/l-lt/LaSOT).

## Download

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

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

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

LaSOT<sub>ext</sub> can also be downloaded from:

* In one zip file: [OneDrive](https://1drv.ms/u/s!Akt_zO4y_u6DgoQrvo5h48AC15l67A?e=Zo6PWx) or [Homepage server](http://vision.cs.stonybrook.edu/~lasot/data/LaSOT_extension_subset.zip)

* One zip file per category: [OneDrive](https://1drv.ms/f/s!Akt_zO4y_u6DgoQZH_aGsNh2f6x6Dg?e=sldyAx)

### Unzip

Unzip all zip files and organize the paths as follows:
```
├── atv
│   ├── atv-1
│   ...
├── badminton
...
```

## Evaluation Metrics and Toolkit

See the [homepage](http://vision.cs.stonybrook.edu/~lasot/results.html) for more information.

arxiv:2009.03465 region:us
{"viewer": false}
TAGS
#arxiv-2009.03465 #region-us 


# Dataset Card for LaSOT-ext

## Dataset Description

- Homepage: LaSOT homepage
- Paper: LaSOT: A High-quality Large-scale Single Object Tracking Benchmark
- 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 new subset provided in the journal version of LaSOT (commonly called LaSOT<sub>ext</sub>), published on IJCV (LaSOT: A High-quality Large-scale Single Object Tracking Benchmark).

For the training/testing splits of LaSOT (conference version), 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<sub>ext</sub> can also be downloaded from:

* In one zip file: OneDrive or Homepage server

* One zip file per category: OneDrive

### Unzip

Unzip all zip files and organize the paths as follows:


## Evaluation Metrics and Toolkit

See the homepage for more information.

# Dataset Card for LaSOT-ext ## Dataset Description

- Homepage: LaSOT homepage
- Paper: LaSOT: A High-quality Large-scale Single Object Tracking Benchmark
- 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 new subset provided in the journal version of LaSOT (commonly called LaSOT<sub>ext</sub>), published on IJCV (LaSOT: A High-quality Large-scale Single Object Tracking Benchmark).

For the training/testing splits of LaSOT (conference version), 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<sub>ext</sub> can also be downloaded from:

* In one zip file: OneDrive or Homepage server

* One zip file per category: OneDrive ### Unzip

Unzip all zip files and organize the paths as follows: ## Evaluation Metrics and Toolkit

See the homepage for more information.
TAGS
#arxiv-2009.03465 #region-us 
 # Dataset Card for LaSOT-ext ## Dataset Description

- Homepage: LaSOT homepage
- Paper: LaSOT: A High-quality Large-scale Single Object Tracking Benchmark
- 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 new subset provided in the journal version of LaSOT (commonly called LaSOT<sub>ext</sub>), published on IJCV (LaSOT: A High-quality Large-scale Single Object Tracking Benchmark).

For the training/testing splits of LaSOT (conference version), 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<sub>ext</sub> can also be downloaded from:

* In one zip file: OneDrive or Homepage server

* One zip file per category: OneDrive ### Unzip

Unzip all zip files and organize the paths as follows: ## Evaluation Metrics and Toolkit

See the homepage for more information.
passage: TAGS
#arxiv-2009.03465 #region-us 
# Dataset Card for LaSOT-ext## Dataset Description

- Homepage: LaSOT homepage
- Paper: LaSOT: A High-quality Large-scale Single Object Tracking Benchmark
- 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 new subset provided in the journal version of LaSOT (commonly called LaSOT<sub>ext</sub>), published on IJCV (LaSOT: A High-quality Large-scale Single Object Tracking Benchmark).

For the training/testing splits of LaSOT (conference version), 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<sub>ext</sub> can also be downloaded from:

* In one zip file: OneDrive or Homepage server

* One zip file per category: OneDrive### Unzip

Unzip all zip files and organize the paths as follows:## Evaluation Metrics and Toolkit

See the homepage for more information.