首个深海视频多目标跟踪基准数据集，助力水下智能追踪研究
DeepSea MOT: A benchmark dataset for multi-object tracking on deep-sea video
- 构建四段深海中层与底栖环境视频，用于评估追踪模型性能
- 采用高阶追踪准确率指标，平衡检测、定位与关联精度
- 开源数据+代码+流程文档，适合海洋观测与视觉算法研究者
在机器学习模型开发中，基准测试多目标追踪与目标检测模型性能至关重要，它使研究人员能通过人工标注的‘测试’数据评估模型表现，实现模型间一致对比并促进性能优化。本研究构建了一个新型基准视频数据集，用于评估多个蒙特雷湾水族馆研究所的目标检测模型以及FathomNet单类目标检测模型，配合多个追踪器进行性能评估。数据集包含四段代表中层和底栖深海生态的视频序列。使用高阶追踪准确率（Higher Order Tracking Accuracy）作为评估指标，该指标兼顾检测、定位与关联准确性。据我们所知，这是首个公开可用的深海视频多目标追踪基准数据集。本文提供基准数据、生成新基准视频的清晰工作流程文档，以及计算指标的示例Python笔记本。
原文摘要 · Abstract (English)
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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