{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T13:14:06Z","timestamp":1723641246728},"reference-count":49,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Communications"],"published-print":{"date-parts":[[2022,2]]},"DOI":"10.1016\/j.comcom.2021.12.007","type":"journal-article","created":{"date-parts":[[2021,12,16]],"date-time":"2021-12-16T06:43:54Z","timestamp":1639637034000},"page":"128-136","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":12,"special_numbering":"C","title":["A lightweight framework for abnormal driving behavior detection"],"prefix":"10.1016","volume":"184","author":[{"given":"Mingliang","family":"Hou","sequence":"first","affiliation":[]},{"given":"Mengyuan","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0003-3740-4829","authenticated-orcid":false,"given":"Wenhong","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Qichao","family":"Ni","sequence":"additional","affiliation":[]},{"given":"Zhen","family":"Cai","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0003-2698-3319","authenticated-orcid":false,"given":"Xiangjie","family":"Kong","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"18","key":"10.1016\/j.comcom.2021.12.007_b1","doi-asserted-by":"crossref","first-page":"6743","DOI":"10.3390\/ijerph17186743","article-title":"An investigation into unsafe behaviors and traffic accidents involving unlicensed drivers: a perspective for alignment measurement","volume":"17","author":"Boulagouas","year":"2020","journal-title":"Int. J. Environ. Res. Public Health"},{"issue":"9\u201310","key":"10.1016\/j.comcom.2021.12.007_b2","doi-asserted-by":"crossref","first-page":"1502","DOI":"10.1080\/07420528.2020.1812623","article-title":"A qualitative study exploring how city bus drivers manage sleepiness and fatigue","volume":"37","author":"Pilkington-Cheney","year":"2020","journal-title":"Chronobiol. Int."},{"issue":"3","key":"10.1016\/j.comcom.2021.12.007_b3","doi-asserted-by":"crossref","first-page":"687","DOI":"10.3390\/s21030687","article-title":"Driving behavior analysis of city buses based on real-time GNSS traces and road information","volume":"21","author":"Yang","year":"2021","journal-title":"Sensors"},{"issue":"2","key":"10.1016\/j.comcom.2021.12.007_b4","doi-asserted-by":"crossref","first-page":"1381","DOI":"10.1007\/s11280-019-00700-1","article-title":"TBI2Flow: Travel behavioral inertia based long-term taxi passenger flow prediction","volume":"23","author":"Kong","year":"2020","journal-title":"World Wide Web"},{"issue":"1","key":"10.1016\/j.comcom.2021.12.007_b5","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1109\/TNSE.2020.3044035","article-title":"Deep matrix factorization for trust-aware recommendation in social networks","volume":"8","author":"Wan","year":"2021","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"issue":"2","key":"10.1016\/j.comcom.2021.12.007_b6","doi-asserted-by":"crossref","first-page":"1902","DOI":"10.1093\/bib\/bbaa043","article-title":"Application of deep learning methods in biological networks","volume":"22","author":"Jin","year":"2020","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.comcom.2021.12.007_b7","first-page":"1","article-title":"FedVCP: A federated-learning-based cooperative positioning scheme for social internet of vehicles","author":"Kong","year":"2021","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"issue":"3","key":"10.1016\/j.comcom.2021.12.007_b8","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1109\/MCOM.2018.1700242","article-title":"Exploring human mobility patterns in urban scenarios: A trajectory data perspective","volume":"56","author":"Xia","year":"2018","journal-title":"IEEE Commun. Mag."},{"key":"10.1016\/j.comcom.2021.12.007_b9","doi-asserted-by":"crossref","DOI":"10.1109\/TII.2021.3067324","article-title":"A federated learning-based license plate recognition scheme for 5G-enabled internet of vehicles","author":"Kong","year":"2021","journal-title":"IEEE Trans. Ind. Inf."},{"key":"10.1016\/j.comcom.2021.12.007_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.comnet.2020.107482","article-title":"Real-time dissemination of emergency warning messages in 5G enabled selfish vehicular social networks","volume":"182","author":"Ullah","year":"2020","journal-title":"Comput. Netw."},{"issue":"2","key":"10.1016\/j.comcom.2021.12.007_b11","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1109\/TAI.2021.3076021","article-title":"Graph learning: A survey","volume":"2","author":"Xia","year":"2021","journal-title":"IEEE Trans. Artif. Intell."},{"issue":"6","key":"10.1016\/j.comcom.2021.12.007_b12","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1109\/MWC.001.1800582","article-title":"Task-driven resource assignment in mobile edge computing exploiting evolutionary computation","volume":"26","author":"Wan","year":"2019","journal-title":"IEEE Wirel. Commun."},{"issue":"7","key":"10.1016\/j.comcom.2021.12.007_b13","doi-asserted-by":"crossref","first-page":"2840","DOI":"10.1109\/TITS.2019.2920962","article-title":"Ranking station importance with human mobility patterns using subway network datasets","volume":"21","author":"Xia","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.comcom.2021.12.007_b14","doi-asserted-by":"crossref","DOI":"10.1109\/JIOT.2021.3051844","article-title":"Real-time mask identification for COVID-19: An edge computing-based deep learning framework","author":"Kong","year":"2021","journal-title":"IEEE Internet Things J."},{"issue":"18","key":"10.1016\/j.comcom.2021.12.007_b15","doi-asserted-by":"crossref","first-page":"5236","DOI":"10.3390\/s20185236","article-title":"Identifying facemask-wearing condition using image super-resolution with classification network to prevent COVID-19","volume":"20","author":"Qin","year":"2020","journal-title":"Sensors"},{"key":"10.1016\/j.comcom.2021.12.007_b16","doi-asserted-by":"crossref","unstructured":"M.S. Ejaz, M.R. Islam, M. Sifatullah, A. Sarker, Implementation of principal component analysis on masked and non-masked face recognition, in: 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology, ICASERT, 2019, pp. 1\u20135.","DOI":"10.1109\/ICASERT.2019.8934543"},{"key":"10.1016\/j.comcom.2021.12.007_b17","series-title":"Recent Trends in Intelligent Computing, Communication and Devices","first-page":"277","article-title":"Face detection based on YOLOv3","author":"Li","year":"2020"},{"key":"10.1016\/j.comcom.2021.12.007_b18","series-title":"YOLOV3: An incremental improvement","author":"Redmon","year":"2018"},{"key":"10.1016\/j.comcom.2021.12.007_b19","doi-asserted-by":"crossref","first-page":"44276","DOI":"10.1109\/ACCESS.2020.2977386","article-title":"A novel GAN-based network for unmasking of masked face","volume":"8","author":"Din","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.comcom.2021.12.007_b20","unstructured":"I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial nets, in: Proceedings of the 27th International Conference on Neural Information Processing Systems, NIPS, 2014, pp. 2672\u20132680."},{"issue":"10","key":"10.1016\/j.comcom.2021.12.007_b21","doi-asserted-by":"crossref","first-page":"1115","DOI":"10.3390\/electronics8101115","article-title":"Interactive removal of microphone object in facial images","volume":"8","author":"Khan","year":"2019","journal-title":"Electronics"},{"issue":"1","key":"10.1016\/j.comcom.2021.12.007_b22","article-title":"A real time face emotion classification and recognition using deep learning model","volume":"1432","author":"Hussain","year":"2020","journal-title":"J. Phys.: Conf. Ser."},{"key":"10.1016\/j.comcom.2021.12.007_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2020.108288","article-title":"A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic","volume":"167","author":"Loey","year":"2021","journal-title":"Measurement"},{"key":"10.1016\/j.comcom.2021.12.007_b24","series-title":"RetinaMask: A Face Mask detector","author":"Jiang","year":"2020"},{"key":"10.1016\/j.comcom.2021.12.007_b25","doi-asserted-by":"crossref","unstructured":"X. Wang, K.C. Chan, K. Yu, C. Dong, C.C. Loy, EDVR: Video restoration with enhanced deformable convolutional networks, in: IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW, 2019, pp. 1954\u20131963.","DOI":"10.1109\/CVPRW.2019.00247"},{"key":"10.1016\/j.comcom.2021.12.007_b26","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.comcom.2021.12.007_b27","doi-asserted-by":"crossref","unstructured":"A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, L. Fei-Fei, Large-scale video classification with convolutional neural networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2014, pp. 1725\u20131732.","DOI":"10.1109\/CVPR.2014.223"},{"key":"10.1016\/j.comcom.2021.12.007_b28","series-title":"Advances in Neural Information Processing Systems (NIPS)","first-page":"568","article-title":"Two-stream convolutional networks for action recognition in videos","author":"Simonyan","year":"2014"},{"issue":"1","key":"10.1016\/j.comcom.2021.12.007_b29","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/0166-2236(92)90344-8","article-title":"Separate visual pathways for perception and action","volume":"15","author":"Goodale","year":"1992","journal-title":"Trends Neurosci."},{"key":"10.1016\/j.comcom.2021.12.007_b30","series-title":"European Conference on Computer Vision, ECCV","first-page":"20","article-title":"Temporal segment networks: Towards good practices for deep action recognition","author":"Wang","year":"2016"},{"issue":"5","key":"10.1016\/j.comcom.2021.12.007_b31","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/S1350-4533(02)00030-9","article-title":"Automatic recognition of alertness and drowsiness from EEG by an artificial neural network","volume":"24","author":"Vuckovic","year":"2002","journal-title":"Med. Eng. Phys."},{"issue":"6","key":"10.1016\/j.comcom.2021.12.007_b32","doi-asserted-by":"crossref","first-page":"5379","DOI":"10.1109\/TVT.2019.2908425","article-title":"Driver activity recognition for intelligent vehicles: A deep learning approach","volume":"68","author":"Xing","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"10.1016\/j.comcom.2021.12.007_b33","doi-asserted-by":"crossref","first-page":"103","DOI":"10.3389\/fnins.2017.00103","article-title":"Improving EEG-based driver fatigue classification using sparse-deep belief networks","volume":"11","author":"Chai","year":"2017","journal-title":"Front. Neurosci."},{"key":"10.1016\/j.comcom.2021.12.007_b34","series-title":"Evrnet: Efficient video restoration on edge devices","author":"Mehta","year":"2020"},{"issue":"1","key":"10.1016\/j.comcom.2021.12.007_b35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3368405","article-title":"Deep learning-based video coding: A review and a case study","volume":"53","author":"Liu","year":"2020","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.comcom.2021.12.007_b36","doi-asserted-by":"crossref","unstructured":"C. Dong, C.C. Loy, K. He, X. Tang, Learning a deep convolutional network for image super-resolution, in: European Conference on Computer Vision, 2014, pp. 184\u2013199.","DOI":"10.1007\/978-3-319-10593-2_13"},{"issue":"7","key":"10.1016\/j.comcom.2021.12.007_b37","doi-asserted-by":"crossref","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","article-title":"Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising","volume":"26","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.comcom.2021.12.007_b38","doi-asserted-by":"crossref","unstructured":"Y. Jo, S.W. Oh, J. Kang, S.J. Kim, Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2018, pp. 3224\u20133232.","DOI":"10.1109\/CVPR.2018.00340"},{"key":"10.1016\/j.comcom.2021.12.007_b39","doi-asserted-by":"crossref","unstructured":"Y. Tian, Y. Zhang, Y. Fu, C. Xu, TDAN: Temporally-deformable alignment network for video super-resolution, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2020, pp. 3360\u20133369.","DOI":"10.1109\/CVPR42600.2020.00342"},{"key":"10.1016\/j.comcom.2021.12.007_b40","doi-asserted-by":"crossref","unstructured":"M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, L.-C. Chen, Mobilenetv2: Inverted residuals and linear bottlenecks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2018, pp. 4510\u20134520.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"10.1016\/j.comcom.2021.12.007_b41","doi-asserted-by":"crossref","unstructured":"S. Zhang, X. Zhu, Z. Lei, H. Shi, X. Wang, S.Z. Li, FaceBoxes: A CPU real-time face detector with high accuracy, in: 2017 IEEE International Joint Conference on Biometrics, IJCB, 2017, pp. 1\u20139.","DOI":"10.1109\/BTAS.2017.8272675"},{"key":"10.1016\/j.comcom.2021.12.007_b42","doi-asserted-by":"crossref","unstructured":"C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna, Rethinking the inception architecture for computer vision, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2016, pp. 2818\u20132826.","DOI":"10.1109\/CVPR.2016.308"},{"issue":"5","key":"10.1016\/j.comcom.2021.12.007_b43","first-page":"29","article-title":"Adaptive real time eye-blink detection system","volume":"99","author":"K\u00a0Galab","year":"2014","journal-title":"Int. J. Comput. Appl."},{"key":"10.1016\/j.comcom.2021.12.007_b44","unstructured":"T. Soukupova, J. Cech, Eye blink detection using facial landmarks, in: 21st Computer Vision Winter Workshop, Rimske Toplice, Slovenia, 2016."},{"issue":"2","key":"10.1016\/j.comcom.2021.12.007_b45","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1007\/s10846-009-9391-1","article-title":"Real-time warning system for driver drowsiness detection using visual information.","volume":"59","author":"Flores","year":"2010","journal-title":"J. Intell. Robot. Syst.: Theory Appl."},{"key":"10.1016\/j.comcom.2021.12.007_b46","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1016\/j.eswa.2016.06.042","article-title":"Dynamic driver fatigue detection using hidden Markov model in real driving condition","volume":"63","author":"Fu","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.comcom.2021.12.007_b47","doi-asserted-by":"crossref","unstructured":"L. King, H. Nguyen, S.K.L. Lal, Early driver fatigue detection from electroencephalography signals using artificial neural networks, in: 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, 2006, pp. 2187\u20132190.","DOI":"10.1109\/IEMBS.2006.259231"},{"issue":"1","key":"10.1016\/j.comcom.2021.12.007_b48","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.ssci.2008.01.007","article-title":"Can SVM be used for automatic EEG detection of drowsiness during car driving?","volume":"47","author":"Yeo","year":"2009","journal-title":"Saf. Sci."},{"key":"10.1016\/j.comcom.2021.12.007_b49","doi-asserted-by":"crossref","unstructured":"M. Hajinoroozi, Z. Mao, Y. Huang, Prediction of driver\u2019s drowsy and alert states from EEG signals with deep learning, in: 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP, 2015, pp. 493\u2013496.","DOI":"10.1109\/CAMSAP.2015.7383844"}],"container-title":["Computer Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0140366421004734?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0140366421004734?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2023,1,4]],"date-time":"2023-01-04T10:42:07Z","timestamp":1672828927000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0140366421004734"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2]]},"references-count":49,"alternative-id":["S0140366421004734"],"URL":"https:\/\/doi.org\/10.1016\/j.comcom.2021.12.007","relation":{},"ISSN":["0140-3664"],"issn-type":[{"value":"0140-3664","type":"print"}],"subject":[],"published":{"date-parts":[[2022,2]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A lightweight framework for abnormal driving behavior detection","name":"articletitle","label":"Article Title"},{"value":"Computer Communications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.comcom.2021.12.007","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2021 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}]}}