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Ego500: The Most Densely Annotated Open-Source Egocentric Action Dataset — Human Archive

ego500.humanarchive.ai · 1,497 words · saved by 1 readers

Egocentric video is emerging as a core data source for robotics, but there is little work on benchmarking data quality or defining what high-quality annotations look like. For this data to be useful for policy training, every clip must: Existing datasets are neither as diverse, as rigorously QA'd, nor as granularly and accurately annotated as Human Archive's datasets. This is made possible by our custom pipelines and human-in-the-loop review process, where over 100 trained annotators at our QC center review, correct, and refine our labels. We then use this labeled data to continually retrain and improve the underlying annotation models. Today, we're releasing HA-Ego-500. Not only do our labels set a new state of the art in temporal accuracy and granularity, but HA-Ego-500 is also the first open-source egocentric dataset to provide large-scale, in-the-wild data across a diverse range of real commercial environments. HA-Ego-500 is a large-scale collection of in-the-wild egocentric video

Egocentric video is emerging as a core data source for robotics, but there is little work on benchmarking data quality or defining what high-quality annotations look like. For this data to be useful for policy training, every clip must: Pass rigorous quality assurance (e.g. hands remain visible, the task is economically useful, lighting is sufficient, and there are no hardware issues). Be temporally accurate, with action boundaries precisely aligned to when each action begins and ends. Contain dense, fine-grained action annotations that describe every sub-action without mislabeling…

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