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arXiv · 2509.03499

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

Kevin Barnard·Elaine Liu·Kristine Walz·Brian Schlining·Nancy Jacobsen Stout·Lonny Lundsten
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Abstract

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.

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Kevin Barnard, Elaine Liu, Kristine Walz, Brian Schlining, Nancy Jacobsen Stout, Lonny Lundsten. 2025-09-03. DeepSea MOT: A benchmark dataset for multi-object tracking on deep-sea video. https://arxiv.org/abs/2509.03499

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