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<!-- views/paperById.ejs --> <!DOCTYPE html> <html> <head> <meta charset="utf-8"> <title>SCITEPRESS - SCIENCE AND TECHNOLOGY PUBLICATIONS</title> <meta name ="description" content="Digital Library" /> <meta name="citation_language" content="en"> <meta name="citation_title" content="CoAx: Collaborative Action Dataset for Human Motion Forecasting in an Industrial Workspace"> <meta name="citation_abstract" content="Human robot collaboration in industrial workspaces where humans perform challenging assembly tasks has become too much; increasingly popular. Now that intention recognition and motion forecasting is being more and more successful in different research fields, we want to transfer that success (and the algorithms making this success possible) to human motion forecasting in an industrial context. Therefore, we present a novel public dataset comprising several industrial assembly tasks, one of which incorporates interaction with a robot. The dataset covers 3 industrial work tasks with robot interaction performed by 6 subjects with 10 repetitions per subject summing up to 1 hour and 58 minutes of video material. We also evaluate the dataset with two baseline methods. One approach is solely velocity-based and the other one is using timeseries classification to infer the future motion of the human worker."> <meta name="citation_publication_date" content="2022/02/03"> <meta name="citation_conference_title" content="International Conference on Agents and Artificial Intelligence"> <meta name="citation_keywords" content="Human Robot Collaboration; Industrial Assembly Dataset; Human Motion Forecasting; Action Recognition;"> <meta name="citation_doi" content="10.5220/0010775600003116"> <meta name="citation_isbn" content="978-989-758-547-0"> <meta name="citation_volume" content="2"> <meta name="citation_firstpage" content="98"> <meta name="citation_lastpage" content="105"> <meta name="citation_publisher" content="SCITEPRESS"> <meta name="citation_author" content="Dimitrios Lagamtzis" > <meta name="citation_author_institution" content="Department of Computer Science and Engineering, Esslingen University, Esslingen, Germany" > <meta name="citation_author" content="Fabian Schmidt" > <meta name="citation_author_institution" content="Department of Computer Science and Engineering, Esslingen University, Esslingen, Germany" > <meta name="citation_author" content="Jan Seyler" > <meta name="citation_author_institution" content="Festo SE & Co. KG, Esslingen, Germany" > <meta name="citation_author" content="Thao Dang" > <meta name="citation_author_institution" content="Department of Computer Science and Engineering, Esslingen University, Esslingen, Germany" > <meta name="citation_abstract_html_url" content="/PublishedPapers/2022/107756"> <meta name="citation_pdf_url" content="/PublishedPapers/2022/107756/107756.pdf"> </head> <body> <article> <a href="/PublishedPapers/2022/107756/pdf/index.html"><h1 class="citation_title">CoAx: Collaborative Action Dataset for Human Motion Forecasting in an Industrial Workspace</h1></a> <h3 class="citation_author"> Dimitrios Lagamtzis, Fabian Schmidt, Jan Seyler, Thao Dang</h3> <h4 class="citation_publication_date">2022</h4> <h4>Abstract</h4> <p class="citation_abstract">Human robot collaboration in industrial workspaces where humans perform challenging assembly tasks has become too much; increasingly popular. Now that intention recognition and motion forecasting is being more and more successful in different research fields, we want to transfer that success (and the algorithms making this success possible) to human motion forecasting in an industrial context. Therefore, we present a novel public dataset comprising several industrial assembly tasks, one of which incorporates interaction with a robot. The dataset covers 3 industrial work tasks with robot interaction performed by 6 subjects with 10 repetitions per subject summing up to 1 hour and 58 minutes of video material. We also evaluate the dataset with two baseline methods. One approach is solely velocity-based and the other one is using timeseries classification to infer the future motion of the human worker.</p> <a href="/PublishedPapers/2022/107756/107756.pdf" class="citation_pdf_url">Download</a> <br /> <br /> <br/> <h4 style="margin:0;">Paper Citation</h4> <br/> <h4 style="margin:0;">in Harvard Style</h4> <p style="margin:0;">Lagamtzis D., Schmidt F., Seyler J. and Dang T. (2022). <b>CoAx: Collaborative Action Dataset for Human Motion Forecasting in an Industrial Workspace</b>. In <i>Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,</i> ISBN 978-989-758-547-0, pages 98-105. DOI: 10.5220/0010775600003116</p> <br/> <h4 style="margin:0;">in Bibtex Style</h4> <p style="margin:0;">@conference{icaart22,<br />author={Dimitrios Lagamtzis and Fabian Schmidt and Jan Seyler and Thao Dang},<br />title={CoAx: Collaborative Action Dataset for Human Motion Forecasting in an Industrial Workspace},<br />booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},<br />year={2022},<br />pages={98-105},<br />publisher={SciTePress},<br />organization={INSTICC},<br />doi={10.5220/0010775600003116},<br />isbn={978-989-758-547-0},<br />}</p> <br/> <h4 style="margin:0;">in EndNote Style</h4> <p style="margin:0;">TY - CONF <br /><br />JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,<br />TI - CoAx: Collaborative Action Dataset for Human Motion Forecasting in an Industrial Workspace<br />SN - 978-989-758-547-0<br />AU - Lagamtzis D. <br />AU - Schmidt F. <br />AU - Seyler J. <br />AU - Dang T. <br />PY - 2022<br />SP - 98<br />EP - 105<br />DO - 10.5220/0010775600003116<br /></p> <br/> </article> </body> </html>