Datasets:
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language:
- en
license: cc-by-sa-4.0
tags:
- biology
- marine
- multi-object-tracking
- video
- underwater
annotations_creators:
- expert-generated
pretty_name: DeepSea MOT
size_categories:
- n<1K
- 1K<n<10K
- 10K<n<100K
task_categories:
- object-detection
---
# DeepSea MOT
**DeepSea MOT** is a benchmark dataset for multi-object tracking on deep-sea video.
## Dataset Description
DeepSea MOT consists of 4 video sequences (2 midwater, 2 benthic) with a total of 2,400 frames and 57,376 annotated objects comprising 188 tracks. The videos were captured by the [Monterey Bay Aquarium Research Institute (MBARI)](https://www.mbari.org/) using remotely operated vehicles (ROVs) [*Doc Ricketts*](https://www.mbari.org/technology/rov-doc-ricketts/) and [*Ventana*](https://www.mbari.org/technology/rov-ventana/) in deep-sea environments, showcasing a variety of marine species and underwater scenes.
- **Paper:** https://arxiv.org/abs/2509.03499
- **Workflow:** https://docs.mbari.org/benchmark_eval/
**[2025-12-08] New 4K Sequences:** In addition to the original sequences, DeepSea MOT now includes two new 4K resolution sequences (Benthic Simple and Midwater Simple) to facilitate research on high-resolution underwater video analysis.
**[2026-02-08] New Marine Debris Sequences:** DeepSea MOT now includes five marine debris sequences to facilitate detection of debris in underwater video. Three video sequences were collected by MBARI (`MD_ROP`, `MD_CAN`, and `MD_CLS`) and two sequences were collected by IFREMER (`MD_BOT` and `MD_FLN`).
*Note: these sequences use a different class ID mapping than the original DeepSea MOT sequences.*
## File Structure
The dataset is organized as follows:
```
data/
├── vidseq_names.txt # TrackEval sequence names file
├── BD/ # Benthic Difficult sequence
│ ├── BD.mov # Source video file
│ ├── gt.txt # MOT Challenge format ground truth
│ ├── seqinfo.ini # TrackEval sequence information
│ ├── images/ # Frame images (JPG format)
│ │ ├── BD_001.jpg
│ │ ├── BD_002.jpg
│ │ └── ...
│ ├── labels/ # YOLO-formatted annotation files (TXT)
│ └── xml/ # Pascal VOC annotation files (from RectLabel)
├── BS/ # Benthic Simple sequence
│ ├── BS.mov
│ ├── gt.txt
│ ├── seqinfo.ini
│ ├── images/
│ ├── labels/
│ └── xml/
├── MWD/ # Midwater Difficult sequence
│ ├── MWD.mov
│ ├── gt.txt
│ ├── seqinfo.ini
│ ├── images/
│ ├── labels/
│ └── xml/
├── MWS/ # Midwater Simple sequence
| ├── MWS.mov
| ├── gt.txt
| ├── seqinfo.ini
| ├── images/
| ├── labels/
| └── xml/
├── ...
```
Each sequence directory contains:
- **Source video** (`.mov`): Original ROV footage
- **Ground truth** (`gt.txt`): MOT Challenge format annotations for tracking evaluation
- **Sequence info** (`seqinfo.ini`): Metadata file for TrackEval compatibility
- **Images** (`images/`): Individual frame extractions in JPG format
- **YOLO labels** (`labels/`): Object detection annotations in YOLO format (TXT files)
- **Pascal VOC** (`xml/`): Object detection annotations in Pascal VOC format (generated via [RectLabel](https://rectlabel.com/))
## Additional Information
### Dataset Curators
Authors of [[1]](https://arxiv.org/abs/2509.03499):
- Kevin Barnard
- Elaine Liu
- Kristine Walz
- Brian Schlining
- Nancy Jacobsen Stout
- Lonny Lundsten
### Citation Information
```bibtex
@article{barnard2025deepseamot,
author = {Barnard, Kevin and Liu, Elaine and Walz, Kristine and Schlining, Brian and Stout, Nancy Jacobsen and Lundsten, Lonny},
title = { {DeepSea MOT}: A benchmark dataset for multi-object tracking on deep-sea video},
year = {2025},
journal = {arXiv preprint arXiv:2509.03499},
doi = {10.48550/arXiv.2509.03499},
}
``` |