{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T04:57:31Z","timestamp":1768193851562,"version":"3.49.0"},"reference-count":31,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"name":"Institute of Information and Communications Technology Planning and Evaluation"},{"DOI":"10.13039\/501100014188","name":"Government of Republic of Korea [Ministry of Science and ICT (MSIT)]","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002582","name":"Artificial Intelligence Graduate School Program, Gwangju Institute of Science and Technology","doi-asserted-by":"publisher","award":["2019-0-01842"],"award-info":[{"award-number":["2019-0-01842"]}],"id":[{"id":"10.13039\/501100002582","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/access.2023.3259992","type":"journal-article","created":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T20:05:02Z","timestamp":1679429102000},"page":"29263-29274","source":"Crossref","is-referenced-by-count":8,"title":["A New Approach to Traffic Accident Anticipation With Geometric Features for Better Generalizability"],"prefix":"10.1109","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7845-3902","authenticated-orcid":false,"given":"Farhan","family":"Mahmood","sequence":"first","affiliation":[{"name":"School of Integrated Technology, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5812-4119","authenticated-orcid":false,"given":"Daehyeon","family":"Jeong","sequence":"additional","affiliation":[{"name":"School of Integrated Technology, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4084-5684","authenticated-orcid":false,"given":"Jeha","family":"Ryu","sequence":"additional","affiliation":[{"name":"School of Integrated Technology, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-54190-7_9"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3413827"},{"key":"ref3","article-title":"When, where, and what? A new dataset for anomaly detection in driving videos","author":"Yao","year":"2020","journal-title":"arXiv:2004.03044"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00371"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2022.3155613"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00271"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-58347-1_2"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/crv.2019.00030"},{"key":"ref9","first-page":"1","article-title":"Quasi-recurrent neural networks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Bradbury"},{"key":"ref10","first-page":"1","article-title":"Convolutional neural networks on graphs with fast localized spectral filtering","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Defferrard"},{"key":"ref11","first-page":"1","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Kipf"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-04167-0_33"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/IROS40897.2019.8967556"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3356995.3364535"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/QoMEX.2016.7498955"},{"key":"ref16","first-page":"1","article-title":"ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Geirhos"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00644"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref19","volume-title":"Video Codec for Audiovisual Services at p x 64 kbit\/s","year":"1993"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1406.1078"},{"key":"ref21","first-page":"1","article-title":"Transforming neural-net output levels to probability distributions","volume-title":"Proc. Neural Inf. Process. Syst.","author":"LeCun"},{"key":"ref22","volume-title":"Bayesian Learning for Neural Networks","volume":"118","author":"Neal","year":"2012"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/AVSS.2018.8639160"},{"key":"ref24","first-page":"1","article-title":"Variational graph recurrent neural networks","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Hajiramezanali"},{"key":"ref25","first-page":"1","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Paszke"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.146"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.10.110"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/10005208\/10077604.pdf?arnumber=10077604","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T17:52:26Z","timestamp":1707846746000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10077604\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":31,"URL":"https:\/\/doi.org\/10.1109\/access.2023.3259992","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}