{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/pku-mmd-a-large-scale-benchmark-for","title":"PKU-MMD: A Large Scale Benchmark for Continuous Multi-Modal Human Action Understanding","arxiv_id":"1703.07475","date":"2017-03-22","proceeding":null,"authors":["Chunhui Liu","Yueyu Hu","Yanghao Li","Sijie Song","Jiaying Liu"],"abstract":"Despite the fact that many 3D human activity benchmarks being proposed, most\nexisting action datasets focus on the action recognition tasks for the\nsegmented videos. There is a lack of standard large-scale benchmarks,\nespecially for current popular data-hungry deep learning based methods. In this\npaper, we introduce a new large scale benchmark (PKU-MMD) for continuous\nmulti-modality 3D human action understanding and cover a wide range of complex\nhuman activities with well annotated information. PKU-MMD contains 1076 long\nvideo sequences in 51 action categories, performed by 66 subjects in three\ncamera views. It contains almost 20,000 action instances and 5.4 million frames\nin total. Our dataset also provides multi-modality data sources, including RGB,\ndepth, Infrared Radiation and Skeleton. With different modalities, we conduct\nextensive experiments on our dataset in terms of two scenarios and evaluate\ndifferent methods by various metrics, including a new proposed evaluation\nprotocol 2D-AP. We believe this large-scale dataset will benefit future\nresearches on action detection for the community.","url_abs":"http://arxiv.org/abs/1703.07475v2","url_pdf":"http://arxiv.org/pdf/1703.07475v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"action-detection","task_name":"Action Detection"},{"task_slug":"action-recognition-in-videos","task_name":"Action Recognition"},{"task_slug":"action-understanding","task_name":"Action Understanding"},{"task_slug":"action-recognition","task_name":"Temporal Action Localization"}],"methods":[],"datasets_introduced":[{"slug":"pku-mmd","name":"PKU-MMD","full_name":"PKU-MMD"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1703.07475","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}