{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T06:58:40Z","timestamp":1775199520399,"version":"3.50.1"},"publisher-location":"Cham","reference-count":60,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030668228","type":"print"},{"value":"9783030668235","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-66823-5_23","type":"book-chapter","created":{"date-parts":[[2021,1,2]],"date-time":"2021-01-02T07:03:14Z","timestamp":1609570994000},"page":"387-405","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":103,"title":["DMD: A Large-Scale Multi-modal Driver Monitoring Dataset for Attention and Alertness Analysis"],"prefix":"10.1007","author":[{"given":"Juan Diego","family":"Ortega","sequence":"first","affiliation":[]},{"given":"Neslihan","family":"Kose","sequence":"additional","affiliation":[]},{"given":"Paola","family":"Ca\u00f1as","sequence":"additional","affiliation":[]},{"given":"Min-An","family":"Chao","sequence":"additional","affiliation":[]},{"given":"Alexander","family":"Unnervik","sequence":"additional","affiliation":[]},{"given":"Marcos","family":"Nieto","sequence":"additional","affiliation":[]},{"given":"Oihana","family":"Otaegui","sequence":"additional","affiliation":[]},{"given":"Luis","family":"Salgado","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,1,3]]},"reference":[{"key":"23_CR1","unstructured":"Abouelnaga, Y., Eraqi, H.M., Moustafa, M.N.: Real-time distracted driver posture classification. In: 32nd Conference on Neural Information Processing Systems (NIPS 2018) (2018)"},{"key":"23_CR2","doi-asserted-by":"crossref","unstructured":"Abraham, H., Reimer, B., Mehler, B.: Advanced driver assistance systems (ADAS): a consideration of driver perceptions on training, usage & implementation. In: Proceedings of the Human Factors and Ergonomics Society, pp. 1954\u20131958 (2017)","DOI":"10.1177\/1541931213601967"},{"issue":"6","key":"23_CR3","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1109\/MSP.2016.2602379","volume":"33","author":"AS Aghaei","year":"2016","unstructured":"Aghaei, A.S., et al.: Smart driver monitoring: when signal processing meets human factors: in the driver\u2019s seat. IEEE Signal Process. Mag. 33(6), 35\u201348 (2016)","journal-title":"IEEE Signal Process. Mag."},{"issue":"2","key":"23_CR4","doi-asserted-by":"publisher","first-page":"965","DOI":"10.1109\/TITS.2013.2247759","volume":"14","author":"C Ahlstrom","year":"2013","unstructured":"Ahlstrom, C., Kircher, K., Kircher, A.: A gaze-based driver distraction warning system and its effect on visual behavior. IEEE Trans. Intell. Transp. Syst. 14(2), 965\u2013973 (2013)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"23_CR5","doi-asserted-by":"crossref","unstructured":"Baccour, M.H., Driewer, F., Kasneci, E., Rosenstiel, W.: Camera-based eye blink detection algorithm for assessing driver drowsiness. In: IEEE Intelligent Vehicles Symposium, pp. 866\u2013872 (2019)","DOI":"10.1109\/IVS.2019.8813871"},{"key":"23_CR6","doi-asserted-by":"crossref","unstructured":"Borghi, G., Venturelli, M., Vezzani, R., Cucchiara, R.: POSEidon: face-from-depth for driver pose estimation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5494\u20135503 (2017)","DOI":"10.1109\/CVPR.2017.583"},{"key":"23_CR7","doi-asserted-by":"crossref","unstructured":"Carreira, J., Zisserman, A.: Quo vadis, action recognition? A new model and the kinetics dataset. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.502"},{"issue":"8","key":"23_CR8","doi-asserted-by":"publisher","first-page":"3055","DOI":"10.1109\/JSEN.2018.2807245","volume":"18","author":"A Chowdhury","year":"2018","unstructured":"Chowdhury, A., Shankaran, R., Kavakli, M., Haque, M.M.: Sensor applications and physiological features in drivers\u2019 drowsiness detection: a review. IEEE Sens. J. 18(8), 3055\u20133067 (2018)","journal-title":"IEEE Sens. J."},{"key":"23_CR9","unstructured":"Craye, C., Karray, F.: Driver distraction detection and recognition using RGB-D sensor. CoRR (2015)"},{"key":"23_CR10","doi-asserted-by":"crossref","unstructured":"Das, N., Ohn-Bar, E., Trivedi, M.M.: On performance evaluation of driver hand detection algorithms: challenges, dataset, and metrics. In: IEEE International Conference on Intelligent Transportation Systems, pp. 2953\u20132958 (2015)","DOI":"10.1109\/ITSC.2015.473"},{"key":"23_CR11","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L., Li, K., Li, F.F.: ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"23_CR12","unstructured":"Deo, N., Trivedi, M.M.: Looking at the driver\/rider in autonomous vehicles to predict take-over readiness. CoRR (2018)"},{"key":"23_CR13","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.asoc.2016.04.027","volume":"45","author":"K Diaz-Chito","year":"2016","unstructured":"Diaz-Chito, K., Hern\u00e1ndez-Sabat\u00e9, A., L\u00f3pez, A.M.: A reduced feature set for driver head pose estimation. Appl. Soft Comput. J. 45, 98\u2013107 (2016)","journal-title":"Appl. Soft Comput. J."},{"key":"23_CR14","doi-asserted-by":"publisher","first-page":"2636","DOI":"10.1073\/pnas.1513271113","volume":"113","author":"TA Dingus","year":"2016","unstructured":"Dingus, T.A., et al.: Driver crash risk factors and prevalence evaluation using naturalistic driving data. Proc. Natl. Acad. Sci. U.S.A. 113, 2636\u20132641 (2016)","journal-title":"Proc. Natl. Acad. Sci. U.S.A."},{"key":"23_CR15","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 (2019)","DOI":"10.1155\/2019\/4125865"},{"key":"23_CR16","unstructured":"European Commission: Roadmap to a single European transport area-towards a competitive and resource efficient transport system. Technical report, European Commission (2011)"},{"key":"23_CR17","unstructured":"Fang, J., Yan, D., Qiao, J., Xue, J.: DADA: a large-scale benchmark and model for driver attention prediction in accidental scenarios (2019)"},{"issue":"11","key":"23_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/s16111805","volume":"16","author":"A Fern\u00e1ndez","year":"2016","unstructured":"Fern\u00e1ndez, A., Usamentiaga, R., Car\u00fas, J.L., Casado, R.: Driver distraction using visual-based sensors and algorithms. Sensors 16(11), 1\u201344 (2016)","journal-title":"Sensors"},{"key":"23_CR19","unstructured":"Fridman, L.: Human-centered autonomous vehicle systems: principles of effective shared autonomy. CoRR (2018)"},{"key":"23_CR20","unstructured":"Goenetxea, J., Unzueta, L., Elordi, U., Ortega, J.D., Otaegui, O.: Efficient monocular point-of-gaze estimation on multiple screens and 3D face tracking for driver behaviour analysis. In: 6th International Conference on Driver Distraction and Inattention, pp. 1\u20138 (2018)"},{"key":"23_CR21","doi-asserted-by":"crossref","unstructured":"Hara, K., Kataoka, H., Satoh, Y.: Can spatiotemporal 3D CNNs retrace the history of 2D CNNs and ImageNet? In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00685"},{"key":"23_CR22","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"23_CR23","unstructured":"Intel: OpenVINO - develop multiplatform computer vision solutions. https:\/\/software.intel.com\/en-us\/openvino-toolkit"},{"key":"23_CR24","doi-asserted-by":"crossref","unstructured":"Jacob\u00e9 de Naurois, C., Bourdin, C., Stratulat, A., Diaz, E., Vercher, J.L.: Detection and prediction of driver drowsiness using artificial neural network models. Accid. Anal. Prev. 126, 95\u2013104 (2019)","DOI":"10.1016\/j.aap.2017.11.038"},{"key":"23_CR25","unstructured":"Janai, J., G\u00fcney, F., Behl, A., Geiger, A.: Computer vision for autonomous vehicles: problems, datasets and state-of-the-art. J. Photogram. Remote Sens. (2017)"},{"key":"23_CR26","doi-asserted-by":"crossref","unstructured":"K\u00f6p\u00fckl\u00fc, O., K\u00f6se, N., Gunduz, A., Rigoll, G.: Resource efficient 3D convolutional neural networks. CoRR (2019)","DOI":"10.1109\/ICCVW.2019.00240"},{"key":"23_CR27","doi-asserted-by":"crossref","unstructured":"K\u00f6p\u00fckl\u00fc, O., K\u00f6se, N., Rigoll, G.: Motion fused frames: data level fusion strategy for hand gesture recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2018)","DOI":"10.1109\/CVPRW.2018.00284"},{"key":"23_CR28","doi-asserted-by":"crossref","unstructured":"K\u00f6se, N., K\u00f6p\u00fckl\u00fc, O., Unnervik, A., Rigoll, G.: Real-time driver state monitoring using a CNN based spatio-temporal approach. In: IEEE Intelligent Transportation Systems Conference (ITSC) (2019)","DOI":"10.1109\/ITSC.2019.8917460"},{"key":"23_CR29","doi-asserted-by":"crossref","unstructured":"Le, T.H.N., Quach, K.G., Zhu, C., Duong, C.N., Luu, K., Savvides, M.: Robust hand detection and classification in vehicles and in the wild. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 1203\u20131210 (2017)","DOI":"10.1109\/CVPRW.2017.159"},{"key":"23_CR30","doi-asserted-by":"crossref","unstructured":"Le, T.H.N., Zheng, Y., Zhu, C., Luu, K., Savvides, M.: Multiple scale faster-RCNN approach to driver\u2019s cell-phone usage and hands on steering wheel detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 46\u201353 (2016)","DOI":"10.1109\/CVPRW.2016.13"},{"key":"23_CR31","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1007\/978-3-030-01264-9_8","volume-title":"Computer Vision \u2013 ECCV 2018","author":"N Ma","year":"2018","unstructured":"Ma, N., Zhang, X., Zheng, H.-T., Sun, J.: ShuffleNet V2: practical guidelines for efficient CNN architecture design. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision \u2013 ECCV 2018. LNCS, vol. 11218, pp. 122\u2013138. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01264-9_8"},{"issue":"3","key":"23_CR32","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1109\/TITS.2016.2582900","volume":"18","author":"B Mandal","year":"2017","unstructured":"Mandal, B., Li, L., Wang, G.S., Lin, J.: Towards detection of bus driver fatigue based on robust visual analysis of eye state. IEEE Trans. Intell. Transp. Syst. 18(3), 545\u2013557 (2017)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"23_CR33","doi-asserted-by":"crossref","unstructured":"Martin, M., et al.: Drive&act: a multi-modal dataset for fine-grained driver behavior recognition in autonomous vehicles. In: The IEEE International Conference on Computer Vision (ICCV), pp. 2801\u20132810 (2019)","DOI":"10.1109\/ICCV.2019.00289"},{"key":"23_CR34","doi-asserted-by":"crossref","unstructured":"Massoz, Q., Langohr, T., Fran\u00e7ois, C., Verly, J.G.: The ULg multimodality drowsiness database (called DROZY) and examples of use. In: IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1\u20137 (2016)","DOI":"10.1109\/WACV.2016.7477715"},{"key":"23_CR35","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1177\/0018720819856454","volume":"62","author":"AD McDonald","year":"2019","unstructured":"McDonald, A.D., Ferris, T.K., Wiener, T.A.: Classification of driver distraction: a comprehensive analysis of feature generation, machine learning, and input measures. Hum. Factors J. Hum. Factors Ergon. Soc. 62, 1019\u20131035 (2019)","journal-title":"Hum. Factors J. Hum. Factors Ergon. Soc."},{"key":"23_CR36","doi-asserted-by":"crossref","unstructured":"Mioch, T., Kroon, L., Neerincx, M.A.: Driver readiness model for regulating the transfer from automation to human control. In: International Conference on Intelligent User Interfaces, IUI, pp. 205\u2013213 (2017)","DOI":"10.1145\/3025171.3025199"},{"key":"23_CR37","doi-asserted-by":"crossref","unstructured":"Ohn-Bar, E., Trivedi, M.M.: The power is in your hands: 3D analysis of hand gestures in naturalistic video. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 912\u2013917 (2013)","DOI":"10.1109\/CVPRW.2013.134"},{"key":"23_CR38","doi-asserted-by":"crossref","unstructured":"Ortega, J.D., Nieto, M., Salgado, L., Otaegui, O.: User-adaptive eyelid aperture estimation for blink detection in driver monitoring systems. In: Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, pp. 342\u2013352. INSTICC, SciTePress (2020)","DOI":"10.5220\/0009369003420352"},{"issue":"7","key":"23_CR39","doi-asserted-by":"publisher","first-page":"1720","DOI":"10.1109\/TPAMI.2018.2845370","volume":"41","author":"A Palazzi","year":"2018","unstructured":"Palazzi, A., Abati, D., Calderara, S., Solera, F., Cucchiara, R.: Predicting the driver\u2019s focus of attention: the DR(eye)VE project. IEEE Trans. Pattern Anal. Mach. Intell. 41(7), 1720\u20131733 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"23_CR40","doi-asserted-by":"publisher","first-page":"900","DOI":"10.1109\/TITS.2019.2901817","volume":"21","author":"A Rasouli","year":"2019","unstructured":"Rasouli, A., Tsotsos, J.K.: Autonomous vehicles that interact with pedestrians: a survey of theory and practice. IEEE Trans. Intell. Transp. Syst. 21, 900\u2013918 (2019)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"23_CR41","doi-asserted-by":"crossref","unstructured":"Roth, M., Gavrila, D.M.: DD-pose - a large-scale driver head pose benchmark. In: IEEE Intelligent Vehicles Symposium, pp. 927\u2013934 (2019)","DOI":"10.1109\/IVS.2019.8814103"},{"key":"23_CR42","unstructured":"SAE International: Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles. Technical report, SAE International (2018)"},{"key":"23_CR43","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: inverted residuals and linear bottlenecks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"issue":"12","key":"23_CR44","doi-asserted-by":"publisher","first-page":"1638","DOI":"10.1109\/TKDE.2007.190663","volume":"19","author":"K Sarinnapakorn","year":"2007","unstructured":"Sarinnapakorn, K., Kubat, M.: Combining subclassifiers in text categorization: a DST-based solution and a case study. IEEE Trans. Knowl. Data Eng. 19(12), 1638\u20131651 (2007)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"23_CR45","doi-asserted-by":"crossref","unstructured":"Schwarz, A., Haurilet, M., Martinez, M., Stiefelhagen, R.: Driveahead \u2013 a large-scale driver head pose dataset. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 1165\u20131174 (2017)","DOI":"10.1109\/CVPRW.2017.155"},{"issue":"6","key":"23_CR46","doi-asserted-by":"publisher","first-page":"2339","DOI":"10.1109\/TITS.2018.2868499","volume":"20","author":"G Sikander","year":"2019","unstructured":"Sikander, G., Anwar, S.: Driver fatigue detection systems: a review. IEEE Trans. Intell. Transp. Syst. 20(6), 2339\u20132352 (2019)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"23_CR47","doi-asserted-by":"crossref","unstructured":"Smith, B., Yin, Q., Feiner, S., Nayar, S.: Gaze locking: passive eye contact detection for human? Object interaction. In: ACM Symposium on User Interface Software and Technology (UIST), pp. 271\u2013280, October 2013","DOI":"10.1145\/2501988.2501994"},{"key":"23_CR48","unstructured":"StateFarm: State Farm Distracted Driver Detection (2016). https:\/\/www.kaggle.com\/c\/state-farm-distracted-driver-detection. Accessed 04 Mar 2020"},{"key":"23_CR49","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, Inception-ResNet and the impact of residual connections on learning. In: AAAI Conference on Artificial Intelligence, pp. 3547\u20133554 (2017)","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"23_CR50","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"23_CR51","doi-asserted-by":"crossref","unstructured":"Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3D convolutional networks. In: IEEE International Conference on Computer Vision (ICCV) (2015)","DOI":"10.1109\/ICCV.2015.510"},{"key":"23_CR52","doi-asserted-by":"crossref","unstructured":"Tran, D., Wang, H., Torresani, L., Ray, J., LeCun, Y., Paluri, M.: A closer look at spatiotemporal convolutions for action recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00675"},{"key":"23_CR53","doi-asserted-by":"crossref","unstructured":"Trutschel, U., Sirois, B., Sommer, D., Golz, M., Edwards, D.: PERCLOS: an alertness measure of the past. In: 6th International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, pp. 172\u2013179 (2011)","DOI":"10.17077\/drivingassessment.1394"},{"key":"23_CR54","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1007\/978-3-319-46484-8_2","volume-title":"Computer Vision \u2013 ECCV 2016","author":"L Wang","year":"2016","unstructured":"Wang, L., et al.: Temporal segment networks: towards good practices for deep action recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 20\u201336. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46484-8_2"},{"issue":"11","key":"23_CR55","doi-asserted-by":"publisher","first-page":"2740","DOI":"10.1109\/TPAMI.2018.2868668","volume":"41","author":"L Wang","year":"2019","unstructured":"Wang, L., et al.: Temporal segment networks for action recognition in videos. IEEE Trans. Pattern Anal. Mach. Intell. 41(11), 2740\u20132755 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"23_CR56","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1007\/978-3-319-54526-4_9","volume-title":"Computer Vision \u2013 ACCV 2016 Workshops","author":"C-H Weng","year":"2017","unstructured":"Weng, C.-H., Lai, Y.-H., Lai, S.-H.: Driver drowsiness detection via a hierarchical temporal deep belief network. In: Chen, C.-S., Lu, J., Ma, K.-K. (eds.) ACCV 2016. LNCS, vol. 10118, pp. 117\u2013133. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-54526-4_9"},{"key":"23_CR57","unstructured":"Xia, Y., Zhang, D., Pozdnukhov, A., Nakayama, K., Zipser, K., Whitney, D.: Training a network to attend like human drivers saves it from common but misleading loss functions, pp. 1\u201314. arXiv: 1711.0 (2017)"},{"key":"23_CR58","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1007\/978-3-030-01267-0_19","volume-title":"Computer Vision \u2013 ECCV 2018","author":"S Xie","year":"2018","unstructured":"Xie, S., Sun, C., Huang, J., Tu, Z., Murphy, K.: Rethinking spatiotemporal feature learning: speed-accuracy trade-offs in video classification. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11219, pp. 318\u2013335. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01267-0_19"},{"issue":"3","key":"23_CR59","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1109\/21.155943","volume":"22","author":"L Xu","year":"1992","unstructured":"Xu, L., Krzyzak, A., Suen, C.Y.: Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Trans. Syst. Man Cybern. 22(3), 418\u2013435 (1992)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"23_CR60","unstructured":"Zhang, X., Sugano, Y., Fritz, M., Bulling, A.: MPIIGaze: real-world dataset and deep appearance-based gaze estimation. CoRR (2017)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-66823-5_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,2]],"date-time":"2025-01-02T00:23:45Z","timestamp":1735777425000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-66823-5_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030668228","9783030668235"],"references-count":60,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-66823-5_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"3 January 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2020.eu\/","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":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5025","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":"1360","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":"27% - 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","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":"7","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)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic. From the ECCV Workshops 249 full papers, 18 short papers, and 21 further contributions were published out of a total of 467 submissions.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}