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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2509.03499 |
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| _version_ | 1866918135122100224 |
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| author | Barnard, Kevin Liu, Elaine Walz, Kristine Schlining, Brian Stout, Nancy Jacobsen Lundsten, Lonny |
| author_facet | Barnard, Kevin Liu, Elaine Walz, Kristine Schlining, Brian Stout, Nancy Jacobsen Lundsten, Lonny |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_03499 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DeepSea MOT: A benchmark dataset for multi-object tracking on deep-sea video Barnard, Kevin Liu, Elaine Walz, Kristine Schlining, Brian Stout, Nancy Jacobsen Lundsten, Lonny Computer Vision and Pattern Recognition 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. |
| title | DeepSea MOT: A benchmark dataset for multi-object tracking on deep-sea video |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.03499 |