# DeepSea MOT: A benchmark dataset for multi-object tracking on deep-sea video

arXiv: https://arxiv.org/abs/2509.03499 · HTML: https://mlsanity.com/paper/2509.03499/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 3 Sep 2025.

## Builds on (most cited first, 3 of 3)

- [BoT-SORT: Robust Associations Multi-Pedestrian Tracking](https://mlsanity.com/paper/2206.14651/) (2022)
- [AnimalTrack: A Benchmark for Multi-Animal Tracking in the Wild](https://mlsanity.com/paper/2205.00158/) (2022)
- [Transparent Object Tracking Benchmark](https://mlsanity.com/paper/2011.10875/) (2020)

## Built on by (most cited first, 1 of 1)

- [ByteTraX: Enhancing the ByteTrack Architecture with Optimised Thresholding](https://mlsanity.com/paper/2609.37801/) (2026)

## Abstract (arXiv)

Benchmarking multi-object tracking and object detection model performance is an essential step in machine learning model development, as it allows researchers to evaluate model detection and tracker performance on human-generated 'test' data, facilitating consistent comparisons between models and trackers and aiding performance optimization. In this study, a novel benchmark video dataset was developed and used to assess the performance of several Monterey Bay Aquarium Research Institute object detection models and a FathomNet single-class object detection model together with several trackers. The dataset consists of four video sequences representing midwater and benthic deep-sea habitats. Performance was evaluated using Higher Order Tracking Accuracy, a metric that balances detection, localization, and association accuracy. To the best of our knowledge, this is the first publicly available benchmark for multi-object tracking in deep-sea video footage. We provide the benchmark data, a clearly documented workflow for generating additional benchmark videos, as well as example Python notebooks for computing metrics.