{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:09:26Z","timestamp":1742911766090,"version":"3.40.3"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031250552"},{"type":"electronic","value":"9783031250569"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-25056-9_36","type":"book-chapter","created":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T12:09:56Z","timestamp":1676376596000},"page":"570-584","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Gesture Recognition with\u00a0Keypoint and\u00a0Radar Stream Fusion for\u00a0Automated Vehicles"],"prefix":"10.1007","author":[{"given":"Adrian","family":"Holzbock","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicolai","family":"Kern","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christian","family":"Waldschmidt","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Klaus","family":"Dietmayer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vasileios","family":"Belagiannis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,15]]},"reference":[{"key":"36_CR1","doi-asserted-by":"publisher","first-page":"72558","DOI":"10.1109\/ACCESS.2020.2987777","volume":"8","author":"KM Abughalieh","year":"2020","unstructured":"Abughalieh, K.M., Alawneh, S.G.: Predicting pedestrian intention to cross the road. IEEE Access 8, 72558\u201372569 (2020)","journal-title":"IEEE Access"},{"key":"36_CR2","unstructured":"Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016)"},{"issue":"2","key":"36_CR3","first-page":"296","volume":"9","author":"T Baek","year":"2022","unstructured":"Baek, T., Lee, Y.G.: Traffic control hand signal recognition using convolution and recurrent neural networks. J. Comput. Des. Eng. 9(2), 296\u2013309 (2022)","journal-title":"J. Comput. Des. Eng."},{"key":"36_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1007\/978-3-319-08849-5_3","volume-title":"Articulated Motion and Deformable Objects","author":"V Belagiannis","year":"2014","unstructured":"Belagiannis, V., Amann, C., Navab, N., Ilic, S.: Holistic human pose estimation with regression forests. In: Perales, F.J., Santos-Victor, J. (eds.) AMDO 2014. LNCS, vol. 8563, pp. 20\u201330. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-08849-5_3"},{"key":"36_CR5","doi-asserted-by":"publisher","unstructured":"Bouazizi, A., Wiederer, J., Kressel, U., Belagiannis, V.: Self-supervised 3D human pose estimation with multiple-view geometry. In: 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021), pp. 1\u20138 (2021). https:\/\/doi.org\/10.1109\/FG52635.2021.9667074","DOI":"10.1109\/FG52635.2021.9667074"},{"key":"36_CR6","doi-asserted-by":"publisher","first-page":"88227","DOI":"10.1109\/ACCESS.2020.2990636","volume":"8","author":"K Geng","year":"2020","unstructured":"Geng, K., Yin, G.: Using deep learning in infrared images to enable human gesture recognition for autonomous vehicles. IEEE Access 8, 88227\u201388240 (2020)","journal-title":"IEEE Access"},{"issue":"8","key":"36_CR7","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"36_CR8","doi-asserted-by":"publisher","unstructured":"Holzbock, A., Tsaregorodtsev, A., Dawoud, Y., Dietmayer, K., Belagiannis, V.: A spatio-temporal multilayer perceptron for gesture recognition. In: 2022 IEEE Intelligent Vehicles Symposium (IV), pp. 1099\u20131106 (2022). https:\/\/doi.org\/10.1109\/IV51971.2022.9827054","DOI":"10.1109\/IV51971.2022.9827054"},{"issue":"8","key":"36_CR9","doi-asserted-by":"publisher","first-page":"902","DOI":"10.1049\/rsn2.12064","volume":"15","author":"RJ de Jong","year":"2021","unstructured":"de Jong, R.J., de Wit, J.J., Uysal, F.: Classification of human activity using radar and video multimodal learning. IET Radar Sonar Navig. 15(8), 902\u2013914 (2021)","journal-title":"IET Radar Sonar Navig."},{"key":"36_CR10","doi-asserted-by":"publisher","unstructured":"Kern, N., Grebner, T., Waldschmidt, C.: PointNet+ LSTM for target list-based gesture recognition with incoherent radar networks. IEEE Trans. Aerosp. Electron. Syst. (2022). https:\/\/doi.org\/10.1109\/TAES.2022.3179248","DOI":"10.1109\/TAES.2022.3179248"},{"issue":"11","key":"36_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LSENS.2020.3033586","volume":"4","author":"N Kern","year":"2020","unstructured":"Kern, N., Steiner, M., Lorenzin, R., Waldschmidt, C.: Robust doppler-based gesture recognition with incoherent automotive radar sensor networks. IEEE Sens. Lett. 4(11), 1\u20134 (2020)","journal-title":"IEEE Sens. Lett."},{"key":"36_CR12","doi-asserted-by":"publisher","first-page":"7125","DOI":"10.1109\/ACCESS.2016.2617282","volume":"4","author":"Y Kim","year":"2016","unstructured":"Kim, Y., Toomajian, B.: Hand gesture recognition using micro-doppler signatures with convolutional neural network. IEEE Access 4, 7125\u20137130 (2016)","journal-title":"IEEE Access"},{"issue":"4","key":"36_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2897824.2925953","volume":"35","author":"J Lien","year":"2016","unstructured":"Lien, J., et al.: Soli: ubiquitous gesture sensing with millimeter wave radar. ACM Trans. Graph. (TOG) 35(4), 1\u201319 (2016)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"36_CR14","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"},{"issue":"23","key":"36_CR15","doi-asserted-by":"publisher","first-page":"7914","DOI":"10.3390\/s21237914","volume":"21","author":"A Mishra","year":"2021","unstructured":"Mishra, A., Kim, J., Cha, J., Kim, D., Kim, S.: Authorized traffic controller hand gesture recognition for situation-aware autonomous driving. Sensors 21(23), 7914 (2021)","journal-title":"Sensors"},{"key":"36_CR16","doi-asserted-by":"crossref","unstructured":"Molchanov, P., Gupta, S., Kim, K., Kautz, J.: Hand gesture recognition with 3D convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1\u20137 (2015)","DOI":"10.1109\/CVPRW.2015.7301342"},{"key":"36_CR17","doi-asserted-by":"crossref","unstructured":"Molchanov, P., Gupta, S., Kim, K., Pulli, K.: Multi-sensor system for driver\u2019s hand-gesture recognition. In: 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), vol. 1, pp. 1\u20138. IEEE (2015)","DOI":"10.1109\/FG.2015.7163132"},{"issue":"13","key":"36_CR18","doi-asserted-by":"publisher","first-page":"15161","DOI":"10.1109\/JSEN.2021.3072680","volume":"21","author":"A Ninos","year":"2021","unstructured":"Ninos, A., Hasch, J., Zwick, T.: Real-time macro gesture recognition using efficient empirical feature extraction with millimeter-wave technology. IEEE Sens. J. 21(13), 15161\u201315170 (2021)","journal-title":"IEEE Sens. J."},{"issue":"6","key":"36_CR19","doi-asserted-by":"publisher","first-page":"2368","DOI":"10.1109\/TITS.2014.2337331","volume":"15","author":"E Ohn-Bar","year":"2014","unstructured":"Ohn-Bar, E., Trivedi, M.M.: Hand gesture recognition in real time for automotive interfaces: a multimodal vision-based approach and evaluations. IEEE Trans. Intell. Transp. Syst. 15(6), 2368\u20132377 (2014)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"36_CR20","unstructured":"Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, pp. 8024\u20138035. Curran Associates, Inc. (2019). http:\/\/papers.neurips.cc\/paper\/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf"},{"key":"36_CR21","doi-asserted-by":"crossref","unstructured":"Pfeuffer, A., Dietmayer, K.: Robust semantic segmentation in adverse weather conditions by means of sensor data fusion. In: 2019 22th International Conference on Information Fusion (FUSION), pp. 1\u20138. IEEE (2019)","DOI":"10.23919\/FUSION43075.2019.9011192"},{"key":"36_CR22","doi-asserted-by":"publisher","first-page":"121930","DOI":"10.1109\/ACCESS.2021.3109255","volume":"9","author":"DT Pham","year":"2021","unstructured":"Pham, D.T., Pham, Q.T., Le, T.L., Vu, H.: An efficient feature fusion of graph convolutional networks and its application for real-time traffic control gestures recognition. IEEE Access 9, 121930\u2013121943 (2021)","journal-title":"IEEE Access"},{"key":"36_CR23","unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J.: Pointnet: deep learning on point sets for 3D classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 652\u2013660 (2017)"},{"key":"36_CR24","doi-asserted-by":"crossref","unstructured":"Qian, K., Zhu, S., Zhang, X., Li, L.E.: Robust multimodal vehicle detection in foggy weather using complementary lidar and radar signals. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 444\u2013453 (2021)","DOI":"10.1109\/CVPR46437.2021.00051"},{"key":"36_CR25","doi-asserted-by":"crossref","unstructured":"Quintero, R., Parra, I., Lorenzo, J., Fern\u00e1ndez-Llorca, D., Sotelo, M.: Pedestrian intention recognition by means of a hidden Markov model and body language. In: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), pp. 1\u20137. IEEE (2017)","DOI":"10.1109\/ITSC.2017.8317766"},{"issue":"3","key":"36_CR26","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(3), 900\u2013918 (2019)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"36_CR27","doi-asserted-by":"publisher","unstructured":"Rohling, H.: Radar CFAR thresholding in clutter and multiple target situations. IEEE Trans. Aerosp. Electron. Syst. AES-19(4), 608\u2013621 (1983). https:\/\/doi.org\/10.1109\/TAES.1983.309350","DOI":"10.1109\/TAES.1983.309350"},{"key":"36_CR28","doi-asserted-by":"publisher","unstructured":"Schreiber, M., Belagiannis, V., Gl\u00e4ser, C., Dietmayer, K.: Motion estimation in occupancy grid maps in stationary settings using recurrent neural networks. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 8587\u20138593 (2020). https:\/\/doi.org\/10.1109\/ICRA40945.2020.9196702","DOI":"10.1109\/ICRA40945.2020.9196702"},{"key":"36_CR29","doi-asserted-by":"crossref","unstructured":"Singh, A.D., Sandha, S.S., Garcia, L., Srivastava, M.: Radhar: human activity recognition from point clouds generated through a millimeter-wave radar. In: Proceedings of the 3rd ACM Workshop on Millimeter-Wave Networks and Sensing Systems, pp. 51\u201356 (2019)","DOI":"10.1145\/3349624.3356768"},{"key":"36_CR30","doi-asserted-by":"crossref","unstructured":"Skaria, S., Al-Hourani, A., Huang, D.: Radar-thermal sensor fusion methods for deep learning hand gesture recognition. In: 2021 IEEE Sensors, pp. 1\u20134. IEEE (2021)","DOI":"10.1109\/SENSORS47087.2021.9639758"},{"key":"36_CR31","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"issue":"16","key":"36_CR32","doi-asserted-by":"publisher","first-page":"12295","DOI":"10.1007\/s00521-019-04408-1","volume":"32","author":"B Vandersmissen","year":"2020","unstructured":"Vandersmissen, B., Knudde, N., Jalalvand, A., Couckuyt, I., Dhaene, T., De Neve, W.: Indoor human activity recognition using high-dimensional sensors and deep neural networks. Neural Comput. Appl. 32(16), 12295\u201312309 (2020)","journal-title":"Neural Comput. Appl."},{"issue":"6","key":"36_CR33","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1109\/MAP.2020.2988528","volume":"62","author":"C Vasanelli","year":"2020","unstructured":"Vasanelli, C., et al.: Calibration and direction-of-arrival estimation of millimeter-wave radars: a practical introduction. IEEE Antennas Propag. Mag. 62(6), 34\u201345 (2020). https:\/\/doi.org\/10.1109\/MAP.2020.2988528","journal-title":"IEEE Antennas Propag. Mag."},{"key":"36_CR34","doi-asserted-by":"crossref","unstructured":"Wang, S., Song, J., Lien, J., Poupyrev, I., Hilliges, O.: Interacting with soli: exploring fine-grained dynamic gesture recognition in the radio-frequency spectrum. In: Proceedings of the 29th Annual Symposium on User Interface Software and Technology, pp. 851\u2013860 (2016)","DOI":"10.1145\/2984511.2984565"},{"issue":"3","key":"36_CR35","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1109\/THMS.2021.3121649","volume":"52","author":"S Wang","year":"2021","unstructured":"Wang, S., Jiang, K., Chen, J., Yang, M., Fu, Z., Yang, D.: Simple but effective: upper-body geometric features for traffic command gesture recognition. IEEE Trans. Hum.-Mach. Syst. 52(3), 423\u2013434 (2021)","journal-title":"IEEE Trans. Hum.-Mach. Syst."},{"key":"36_CR36","doi-asserted-by":"crossref","unstructured":"Wharton, Z., Behera, A., Liu, Y., Bessis, N.: Coarse temporal attention network (CTA-Net) for driver\u2019s activity recognition. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 1279\u20131289 (2021)","DOI":"10.1109\/WACV48630.2021.00132"},{"key":"36_CR37","doi-asserted-by":"crossref","unstructured":"Wiederer, J., Bouazizi, A., Kressel, U., Belagiannis, V.: Traffic control gesture recognition for autonomous vehicles. In: 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 10676\u201310683. IEEE (2020)","DOI":"10.1109\/IROS45743.2020.9341214"},{"key":"36_CR38","doi-asserted-by":"publisher","unstructured":"Winkler, V.: Range doppler detection for automotive FMCW radars. In: European Radar Conference, pp. 166\u2013169. IEEE, Piscataway (2007). https:\/\/doi.org\/10.1109\/EURAD.2007.4404963","DOI":"10.1109\/EURAD.2007.4404963"},{"key":"36_CR39","unstructured":"Wu, Y., Kirillov, A., Massa, F., Lo, W.Y., Girshick, R.: Detectron2 (2019). https:\/\/github.com\/facebookresearch\/detectron2"},{"key":"36_CR40","doi-asserted-by":"crossref","unstructured":"Xu, F., Xu, F., Xie, J., Pun, C.M., Lu, H., Gao, H.: Action recognition framework in traffic scene for autonomous driving system. IEEE Trans. Intell. Transp. Syst. (2021)","DOI":"10.1109\/TITS.2021.3135251"},{"issue":"1","key":"36_CR41","doi-asserted-by":"publisher","first-page":"59","DOI":"10.3390\/s19010059","volume":"19","author":"N Zengeler","year":"2018","unstructured":"Zengeler, N., Kopinski, T., Handmann, U.: Hand gesture recognition in automotive human-machine interaction using depth cameras. Sensors 19(1), 59 (2018)","journal-title":"Sensors"},{"issue":"8","key":"36_CR42","doi-asserted-by":"publisher","first-page":"3278","DOI":"10.1109\/JSEN.2018.2808688","volume":"18","author":"Z Zhang","year":"2018","unstructured":"Zhang, Z., Tian, Z., Zhou, M.: Latern: dynamic continuous hand gesture recognition using FMCW radar sensor. IEEE Sens. J. 18(8), 3278\u20133289 (2018)","journal-title":"IEEE Sens. J."}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-25056-9_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T18:36:23Z","timestamp":1710268583000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-25056-9_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031250552","9783031250569"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-25056-9_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"15 February 2023","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":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","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":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","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":"5804","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":"1645","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":"28% - 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":"3.21","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":"3.91","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":"From the workshops, 367 reviewed full papers have been selected for publication","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)"}}]}}