{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:40:03Z","timestamp":1775666403781,"version":"3.50.1"},"reference-count":52,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100001775","name":"Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and I.T., University of Technology Sydney","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001775","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Researchers Supporting Project","award":["RSP-2021\/14"],"award-info":[{"award-number":["RSP-2021\/14"]}]},{"DOI":"10.13039\/501100002383","name":"King Saud University, Riyadh, Saudi Arabia","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002383","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2021.3096032","type":"journal-article","created":{"date-parts":[[2021,7,9]],"date-time":"2021-07-09T19:41:43Z","timestamp":1625859703000},"page":"99312-99326","source":"Crossref","is-referenced-by-count":12,"title":["ADMT: Advanced Driver\u2019s Movement Tracking System Using Spatio-Temporal Interest Points and Maneuver Anticipation Using Deep Neural Networks"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3882-7030","authenticated-orcid":false,"given":"Shilpa","family":"Gite","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9863-2054","authenticated-orcid":false,"given":"Biswajeet","family":"Pradhan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdullah","family":"Alamri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2653-3780","authenticated-orcid":false,"given":"Ketan","family":"Kotecha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.460"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2018.03.056"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.365"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.107099"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2020.2973361"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2016.08.003"},{"key":"ref37","article-title":"Deep, convolutional, and recurrent models for human activity recognition using wearables","author":"hammerla","year":"2016","journal-title":"arXiv 1604 08880"},{"key":"ref36","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1007\/978-3-642-25446-8_4","article-title":"Sequential deep learning for human action recognitionn","volume":"7065","author":"baccouche","year":"2011","journal-title":"Human Behavior Understanding"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2009.5459361"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/VSPETS.2005.1570899"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.4018\/ijmdem.2015010101"},{"key":"ref27","author":"pitas","year":"0","journal-title":"Digital Image Processing Algorithms and Applications"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2020.2965434"},{"key":"ref2","year":"2021"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2014.2325776"},{"key":"ref20","article-title":"Characterizing driving styles with deep learning","author":"dong","year":"2016","journal-title":"arXiv 1607 03611"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2018.2804160"},{"key":"ref21","first-page":"11","article-title":"Early prediction of driver’s action using deep neural networks","volume":"9","author":"gite","year":"2019","journal-title":"Int J Inf Retr Res"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"666","DOI":"10.1109\/TITS.2017.2706978","article-title":"Driving style recognition for intelligent vehicle control and advanced driver assistance: A survey","volume":"19","author":"martinez","year":"2016","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966210"},{"key":"ref26","article-title":"MIT advanced vehicle technology study: Large-scale naturalistic driving study of driver behavior and interaction with automation","author":"fridman","year":"2017","journal-title":"arXiv 1711 06976"},{"key":"ref25","article-title":"Vision-based human action recognition using machine learning techniques","author":"bux","year":"2017"},{"key":"ref50","article-title":"A survey of recent advances in face detection","author":"zhang","year":"2010"},{"key":"ref51","article-title":"Fast multi-view face detection","volume":"3","author":"jones","year":"2003"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.5334\/dsj-2019-055"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2010.2092770"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-81-322-2526-3_10"},{"key":"ref40","first-page":"20","article-title":"Temporal segment networks: Towards good practices for deep action recognition","author":"wang","year":"2016","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2430335"},{"key":"ref13","first-page":"792","article-title":"Learning spatiotemporal structure from RGB-D videos for human activity detection and anticipation","author":"koppula","year":"2013","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.364"},{"key":"ref15","article-title":"Brain4Cars: Car that knows before you do via sensory-fusion deep learning architecture","author":"jain","year":"2016","journal-title":"arXiv 1601 00740"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2712789"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ICAR.2015.7251499"},{"key":"ref18","first-page":"573","article-title":"Developing a novel algorithm for identifying driver’s behavior in ADAS using deep learning","volume":"10","author":"virmani","year":"2017","journal-title":"IJCTA"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/2905055.2905185"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2014.6856477"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.24"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2013.2263400"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/MPRV.2011.38"},{"key":"ref8","first-page":"1110","article-title":"Hierarchical recurrent neural network for skeleton based action recognition","author":"du","year":"2015","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit (CVPR)"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ACC.2012.6315654"},{"key":"ref49","first-page":"13472","article-title":"Face detection and tracking using KLT and Viola Jones","volume":"11","author":"boda","year":"2016","journal-title":"ARPN J Eng Appl Sci"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2013.2247760"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1049\/iet-cvi.2015.0316"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ICARCV.2010.5707319"},{"key":"ref48","first-page":"82","article-title":"Recurrent convolutional neural networks for scene labeling","author":"pinheiro","year":"2014","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298958"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref41","first-page":"29","article-title":"Sequential deep learning for human action recognition","author":"baccouche","year":"2011","journal-title":"Proc Int Workshop Hum Behav Understand"},{"key":"ref44","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"lecun","year":"2015","journal-title":"Nature"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/s13748-019-00177-z"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09478887.pdf?arnumber=9478887","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T17:40:41Z","timestamp":1643218841000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9478887\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":52,"URL":"https:\/\/doi.org\/10.1109\/access.2021.3096032","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}