DeepSea MOT data

.gitattributes

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 # Video files - compressed
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+*.mov filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,53 @@
 ---
+language:
+- en
 license: mit
+tags:
+- biology
+- marine
+- multi-object-tracking
+- video
+- underwater
+annotations_creators:
+- expert-generated
+pretty_name: DeepSea MOT
+size_categories:
+- n<1K
+task_categories:
+- object-detection
 ---
+
+# Dataset Card for 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/TBD
+
+## Additional Information
+
+### Dataset Curators
+
+Authors of [[1]](https://arxiv.org/abs/TBD):
+
+- 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:2501.XXXXX},
+    doi = {10.48550/arXiv.2501.XXXXX},
+}
+```

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