{"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/a-realistic-dataset-and-baseline-temporal","title":"A Realistic Dataset and Baseline Temporal Model for Early Drowsiness Detection","arxiv_id":"1904.07312","date":"2019-04-15","proceeding":null,"authors":["Reza Ghoddoosian","Marnim Galib","Vassilis Athitsos"],"abstract":"Drowsiness can put lives of many drivers and workers in danger. It is\nimportant to design practical and easy-to-deploy real-world systems to detect\nthe onset of drowsiness.In this paper, we address early drowsiness detection,\nwhich can provide early alerts and offer subjects ample time to react. We\npresent a large and public real-life dataset of 60 subjects, with video\nsegments labeled as alert, low vigilant, or drowsy. This dataset consists of\naround 30 hours of video, with contents ranging from subtle signs of drowsiness\nto more obvious ones. We also benchmark a temporal model for our dataset, which\nhas low computational and storage demands. The core of our proposed method is a\nHierarchical Multiscale Long Short-Term Memory (HM-LSTM) network, that is fed\nby detected blink features in sequence. Our experiments demonstrate the\nrelationship between the sequential blink features and drowsiness. In the\nexperimental results, our baseline method produces higher accuracy than human\njudgment.","url_abs":"http://arxiv.org/abs/1904.07312v1","url_pdf":"http://arxiv.org/pdf/1904.07312v1.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":[{"paper_slug":"a-realistic-dataset-and-baseline-temporal","repo_url":"https://github.com/Mahima18/AI_Project_Team9","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"a-realistic-dataset-and-baseline-temporal","repo_url":"https://github.com/m2rik/Drowsiness-Detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"a-realistic-dataset-and-baseline-temporal","repo_url":"https://github.com/raj2289/Driver-drowsiness-detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"a-realistic-dataset-and-baseline-temporal","repo_url":"https://github.com/rezaghoddoosian/Early-Drowsiness-Detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"a-realistic-dataset-and-baseline-temporal","repo_url":"https://github.com/steffytw/machine-learning-iris","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"a-realistic-dataset-and-baseline-temporal","repo_url":"https://github.com/tuanhm11299/Drowsiness-Detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[{"slug":"uta-rldd","name":"UTA-RLDD","full_name":"University of Texas at Arlington Real-Life Drowsiness Dataset"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1904.07312","atlas_url":"https://app.syntology.ai/?focus=1904.07312","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}