---
annotations_creators: []
language: en
size_categories:
- n<1K
task_categories:
- image-classification
task_ids: []
pretty_name: WebUOT-238-Test
tags:
- fiftyone
- image-classification
- video
dataset_summary: '




  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 238 samples.


  ## Installation


  If you haven''t already, install FiftyOne:


  ```bash

  pip install -U fiftyone

  ```


  ## Usage


  ```python

  import fiftyone as fo

  from fiftyone.utils.huggingface import load_from_hub


  # Load the dataset

  # Note: other available arguments include ''max_samples'', etc

  dataset = load_from_hub("Voxel51/WebUOT-238-Test")


  # Launch the App

  session = fo.launch_app(dataset)

  ```

  '
---

# Dataset Card for WebUOT-238-Test



This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 238 samples.

## Installation

If you haven't already, install FiftyOne:

```bash
pip install -U fiftyone
```

## Usage

```python
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub

# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("Voxel51/WebUOT-238-Test")

# Launch the App
session = fo.launch_app(dataset)
```


### Dataset Description

WebUOT-1M is the largest million-scale benchmark for underwater object tracking (UOT), designed to address limitations in existing datasets by providing diverse underwater scenarios, rich annotations, and language prompts. It comprises **1.1 million frames** across **1,500 underwater videos**, covering **408 target categories** categorized into 12 superclasses (e.g., fish, molluscs, inanimate objects). The dataset includes high-quality bounding box annotations, 23 tracking attributes (e.g., illumination variation, camouflage), and language descriptions for multimodal tracking research.

**Note:** This dataset, which has been parsed into FiftyOne format, comprises 238 randomly selected videos from the WebUOT-1M test set for a total of 192,000+ frames.

### Dataset Details

- **Curated by:**  
  Chunhui Zhang (Shanghai Jiao Tong University), Li Liu (HKUST-Guangzhou), Guanjie Huang (HKUST-Guangzhou), Hao Wen (CloudWalk), Xi Zhou (CloudWalk), Yanfeng Wang (Shanghai Jiao Tong University).  
- **Funded by:**  
  National Natural Science Foundation of China (No. 62101351), Key R&D Program of Chongqing (cstc2021jscx-gksbX0032).  
- **Language(s):**  
  English (annotations and language prompts).  
- **License:**  
  [Creative Commons (intended for academic research).](https://creativecommons.org/licenses/by/4.0/)
- **Shared by:** [Harpreet Sahota, Hacker-in-Residence @ Voxel51](https://huggingface.co/harpreetsahota)

### Dataset Sources
- **Repository:** https://github.com/983632847/Awesome-Multimodal-Object-Tracking/tree/main/WebUOT-1M
- **Paper:** https://arxiv.org/abs/2405.19818

## Uses

### Direct Use

- Training/evaluating UOT algorithms.  
- Multimodal tracking (vision + language prompts).  
- Studying domain adaptation (underwater vs. open-air environments).  
- Marine conservation, underwater robotics, and search/rescue applications.  

### Out-of-Scope Use

- Non-underwater tracking tasks (e.g., aerial/terrestrial tracking).  
- Commercial applications without proper licensing.  
- Non-visual tasks (e.g., audio analysis).  

## Dataset Structure

- **Fields:**  
  - Videos: 1,500 clips (1,020 train / 480 test).  
  - Annotations: Bounding boxes, absent labels, 23 attributes (e.g., low visibility, similar distractors).  
  - Language Prompts: Text descriptions of targets (e.g., "red clownfish in yellow coral").  
  - Metadata: Object categories (408), superclasses (12), resolution, duration.  
- **Splits:**  
  Train/Test sets divided by videos, ensuring no overlap in categories or scenarios.  

## Dataset Creation

### Curation Rationale

To bridge the gap in UOT research caused by small-scale datasets, WebUOT-1M was created to enable robust model training/evaluation, domain adaptation, and multimodal tracking in complex underwater environments.  

### Source Data

#### Data Collection and Processing

- **Sources:** YouTube, Bilibili (filtered for diversity).  
- **Processing:**  
  - Manual selection of moving targets.  
  - Semi-supervised enhancement for blurry/low-visibility frames.  
  - Professional annotation team for bounding boxes and attributes.  
  - Final verification by authors.  

#### Who are the source data producers?  

Videos were captured by divers, underwater robots, and hobbyists using varied devices (cameras, phones).  

### Annotations

#### Annotation Process  

- **Tools:** In-house annotation tools; enhanced frames for challenging cases.  
- **Guidelines:** Focus on target motion, bounding box accuracy, and attribute labeling (23 attributes).  
- **Validation:** Multiple rounds of correction by authors.  

#### Who are the annotators?  

A professional labeling team and the authors performed verification.

## Citation

**BibTeX:**

```bibtex
@article{zhang2024webuot,
  title={WebUOT-1M: Advancing Deep Underwater Object Tracking with A Million-Scale Benchmark},
  author={Zhang, Chunhui and Liu, Li and Huang, Guanjie and Wen, Hao and Zhou, Xi and Wang, Yanfeng},
  journal={arXiv preprint arXiv:2405.19818},
  year={2024}
}
```


## Glossary

The following glossary details the attributes of each video.

Here's the content parsed as a markdown table:

| Attribute | Definition |
|-----------|------------|
| 01. LR | If the size of the bounding box of the target in one frame is less than 400 pixels. |
| 02. FM | The center position of the target in two consecutive frames exceeds 20 pixels. |
| 03. SV | The ratio of the target bounding box is not within the range [0.5, 2]. |
| 04. ARV | The aspect ratio of the target bounding box is not in the range [0.5, 2]. |
| 05. CM | There is severe camera movement in the video frame. |
| 06. VC | Viewpoint changes significantly affect the appearance of the target. |
| 07. PO | If the target appears partially occluded in one frame. |
| 08. FO | As long as the target is completely occluded in one frame. |
| 09. OV | There is one frame where the target completely leaves the video frame. |
| 10. ROT | The target rotates in the video frame. |
| 11. DEF | The target appears deformation in the video frame. |
| 12. SD | Similarity interference appears around the target. |
| 13. IV | The illumination of the target area changes significantly. |
| 14. MB | The target area becomes blurred due to target motion or camera motion. |
| 15. PTI | In the initial frame only partial information about the target is visible. |
| 16. NAO | The target belongs to a natural or artificial object. |
| 17. CAM | The target is camouflaging in the video frame. |
| 18. UV | The underwater visibility of the target area (low, medium, or high visibility). |
| 19. WCV | The color of the water of the target area. |
| 20. US | Different underwater scenarios where the target is located. |
| 21. SP | Different shooting perspectives (underwater, outside-water, and fish-eye views). |
| 22. SIZ | The size s = √(w × h) of the video is small (s < √(640 × 480)), medium (√(640 × 480) ≤ s < √(1280 × 720)), or large (s ≥ √(1280 × 720)). |
| 23. LEN | The length l of the video is short (l ≤ 600 frames), medium (600 frames < l ≤ 1800 frames), or long (l > 1800 frames). |