{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T04:25:27Z","timestamp":1743049527758,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031059353"},{"type":"electronic","value":"9783031059360"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-05936-0_32","type":"book-chapter","created":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T16:07:09Z","timestamp":1652198829000},"page":"405-416","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SelectAug: A Data Augmentation Method for\u00a0Distracted Driving Detection"],"prefix":"10.1007","author":[{"given":"Yuan","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Mi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingguo","family":"Ge","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingyuan","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daoqing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tong","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,5,11]]},"reference":[{"key":"32_CR1","unstructured":"National Highway Traffic Safety Administration: Traffic safety facts 2019 data: Distracted Driving 2019 (DOT HS 813 111). Washington, DC (2021)"},{"key":"32_CR2","doi-asserted-by":"crossref","unstructured":"Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V.: Learning transferable architectures for scalable image recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2017)","DOI":"10.1109\/CVPR.2018.00907"},{"key":"32_CR3","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. arXiv preprint arXiv:1709.01507 (2017)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"32_CR4","doi-asserted-by":"crossref","unstructured":"Mohamed, A., Qian, K., Elhoseiny, M., et al.: Social-STGCNN: a social spatio-temporal graph convolutional neural network for human trajectory prediction. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2020)","DOI":"10.1109\/CVPR42600.2020.01443"},{"key":"32_CR5","unstructured":"Hendrycks, D., et al.: AugMix: a simple data processing method to improve robustness and uncertainty. In: ICLR (2020)"},{"key":"32_CR6","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., et al.: mixup: Beyond empirical risk minimization. In: ICLR (2018)"},{"key":"32_CR7","unstructured":"Zhong, Z., Zheng, L., Kang, G., Li, S., Yang, Y.: Random erasing data augmentation. arXiv preprint arXiv:1708.04896 (2017)"},{"key":"32_CR8","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., et al.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), Miami, Florida, USA, June 20\u201325 2009. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"32_CR9","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"32_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"32_CR11","doi-asserted-by":"crossref","unstructured":"Yun, S., Han, D., Chun, S., et al.: CutMix: regularization strategy to train strong classifiers with localizable features. In: International Conference on Computer Vision, 0. ICLR (2019)","DOI":"10.1109\/ICCV.2019.00612"},{"key":"32_CR12","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"32_CR13","doi-asserted-by":"crossref","unstructured":"Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2017)","DOI":"10.1109\/CVPR.2017.195"},{"issue":"4","key":"32_CR14","doi-asserted-by":"publisher","first-page":"1100","DOI":"10.1007\/s10489-019-01603-4","volume":"50","author":"M Lu","year":"2019","unstructured":"Lu, M., Hu, Y., Lu, X.: Driver action recognition using deformable and dilated faster R-CNN with optimized region proposals. Appl. Intell. 50(4), 1100\u20131111 (2019). https:\/\/doi.org\/10.1007\/s10489-019-01603-4","journal-title":"Appl. Intell."},{"issue":"5","key":"32_CR15","doi-asserted-by":"publisher","first-page":"851","DOI":"10.1007\/s00138-018-0994-z","volume":"30","author":"Y Hu","year":"2018","unstructured":"Hu, Y., Lu, M., Lu, X.: Driving behaviour recognition from still images by using multi-stream fusion CNN. Mach. Vis. Appl. 30(5), 851\u2013865 (2018). https:\/\/doi.org\/10.1007\/s00138-018-0994-z","journal-title":"Mach. Vis. Appl."},{"issue":"2","key":"32_CR16","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1049\/iet-cvi.2015.0175","volume":"10","author":"C Yan","year":"2016","unstructured":"Yan, C., Coenen, F., Zhang, B.: Driving posture recognition by convolutional neural networks. IET Comput. Vis. 10(2), 103\u2013114 (2016)","journal-title":"IET Comput. Vis."},{"key":"32_CR17","doi-asserted-by":"crossref","unstructured":"Bolya, D., Zhou, C., Xiao, F., et al.: YOLACT++: better real-time instance segmentation. IEEE Trans. Pattern Anal. Mach. Intell. PP(99), 1 (2020)","DOI":"10.1109\/ICCV.2019.00925"},{"key":"32_CR18","unstructured":"Kaggle Competition: State Farm Distracted Driver Detection. https:\/\/www.kaggle.com\/c\/state-farm-distracted-driver-detection. Accessed 12 Apr 2017"},{"key":"32_CR19","doi-asserted-by":"crossref","unstructured":"Baheti, B., Gajre, S., Talbar, S.: Detection of distracted driver using convolutional neural network. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE (2018)","DOI":"10.1109\/CVPRW.2018.00150"},{"key":"32_CR20","doi-asserted-by":"crossref","unstructured":"Pohl, J., Birk, W., Westervall, L.: A driver-distraction-based lane-keeping assistance system. Proc. Inst. Mech. Eng. Part I J. Syst. Control Eng. 221, 541\u2013552 (2007)","DOI":"10.1243\/09596518JSCE218"},{"key":"32_CR21","doi-asserted-by":"crossref","unstructured":"Alotaibi, M., Alotaibi, B.: Distracted driver classification using deep learning. Signal Image Video Process. 14, 617\u2013624 (2019)","DOI":"10.1007\/s11760-019-01589-z"},{"issue":"8","key":"32_CR22","doi-asserted-by":"publisher","first-page":"6645","DOI":"10.1109\/TVT.2017.2660497","volume":"66","author":"J Hu","year":"2017","unstructured":"Hu, J., Xu, L., He, X., et al.: Abnormal driving detection based on normalised driving behaviour. IEEE Trans. Veh. Technol. 66(8), 6645\u20136652 (2017)","journal-title":"IEEE Trans. Veh. Technol."},{"key":"32_CR23","doi-asserted-by":"crossref","unstructured":"Eren, H., Celik, U., Poyraz, M.: Stereo vision and statistical based behaviour prediction of driver. In: Proceedings of the 2007 IEEE Intelligent Vehicles Symposium, Istanbul, Turkey, June 13\u201315 2007, pp. 657\u2013662 (2007)","DOI":"10.1109\/IVS.2007.4290191"},{"key":"32_CR24","doi-asserted-by":"crossref","unstructured":"Murphy-Chutorian, E., Doshi, A., Trivedi, M.M.: Head pose estimation for driver assistance systems: a robust algorithm and experimental evaluation. In: Intelligent Transportation Systems Conference, pp. 709\u2013714. IEEE (2007)","DOI":"10.1109\/ITSC.2007.4357803"},{"key":"32_CR25","doi-asserted-by":"crossref","unstructured":"Eraqi, H.M., Abouelnaga, Y., Saad, M.H., Moustafa, M.N.: Driver distraction identification with an ensemble of convolutional neural networks. J. Adv. Transp. (2019)","DOI":"10.1155\/2019\/4125865"},{"key":"32_CR26","doi-asserted-by":"crossref","unstructured":"Berri, R.A., Silva, A.G., Parpinelli, R.S., Girardi, E., Arthur, R.: A pattern recognition system for detecting use of mobile phones while driving. In: International Conference on Computer Vision Theory and Applications, VISAPP, pp. 411\u2013418. IEEE (2014)","DOI":"10.5220\/0004684504110418"},{"key":"32_CR27","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1049\/iet-its.2011.0116","volume":"6","author":"CH Zhao","year":"2012","unstructured":"Zhao, C.H., Zhang, B.L., He, J., Lian, J.: Recognition of driving postures by contourlet transform and random forests. IET Intell. Transp. Syst. 6, 161\u2013168 (2012)","journal-title":"IET Intell. Transp. Syst."}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-05936-0_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T08:02:55Z","timestamp":1727164975000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-05936-0_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031059353","9783031059360"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-05936-0_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"11 May 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chengdu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 May 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 May 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/pakdd.net\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"558","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"121","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"22% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.75","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6.45","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}