{"url":"/dataset/ha-vid","name":"HA-ViD","full_name":"HA-ViD: A Human Assembly Video Dataset","description_markdown":"Understanding comprehensive assembly knowledge from videos is critical for futuristic ultra-intelligent industry. To enable technological breakthrough, we present HA-ViD – an assembly video dataset that features representative industrial assembly scenarios, natural procedural knowledge acquisition process, and consistent human-robot shared annotations. Specifically, HA-ViD captures diverse collaboration patterns of real-world assembly, natural human behaviors and learning progression during assembly, and granulate action annotations to subject, action verb, manipulated object, target object, and tool. We provide 3222 multi-view and multi-modality videos, 1.5M frames, 96K temporal labels and 2M spatial labels. We benchmark four foundational video understanding tasks: action recognition, action segmentation, object detection and multi-object tracking. Importantly, we analyze their performance and the further reasoning steps for comprehending knowledge in assembly progress, process efficiency, task collaboration, skill parameters and human intention. Details of HA-ViD is available at: https://iai-hrc.github.io/ha-vid.","description_withheld":null,"homepage":"https://iai-hrc.github.io/ha-vid","introduced_date":"2023-07-09","introduced_date_note":null,"introduced_by":null,"license":{"name":"CC BY-NC 4.0","url":"https://creativecommons.org/licenses/by-nc/4.0/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Videos","url":"/datasets/modality/videos"},{"name":"RGB-D","url":"/datasets/modality/rgb-d"},{"name":"Actions","url":"/datasets/modality/actions"},{"name":"Tracking","url":"/datasets/modality/tracking"},{"name":"RGB Video","url":"/datasets/modality/rgb-video"}],"tasks":[{"name":"Object Detection","url":"/task/object-detection","datasets_with_task":"/datasets/task/object-detection"},{"name":"Action Recognition","url":"/task/action-recognition-in-videos","datasets_with_task":"/datasets/task/action-recognition-in-videos"},{"name":"Multi-Object Tracking","url":"/task/multi-object-tracking","datasets_with_task":"/datasets/task/multi-object-tracking"},{"name":"Action Segmentation","url":"/task/action-segmentation","datasets_with_task":"/datasets/task/action-segmentation"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["HA-ViD"],"data_loaders":[{"repo":"https://github.com/pytorch/examples","url":"https://github.com/pytorch/examples","frameworks":["pytorch"]},{"repo":"https://github.com/song520Y/datasets","url":"https://github.com/song520Y/datasets","frameworks":["pytorch"]}],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}