{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T09:52:27Z","timestamp":1756461147143,"version":"3.41.0"},"publisher-location":"Cham","reference-count":52,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031925900","type":"print"},{"value":"9783031925917","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-92591-7_13","type":"book-chapter","created":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T07:24:30Z","timestamp":1747985070000},"page":"203-217","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["OPPH: A Vision-Based Operator for\u00a0Measuring Body Movements for\u00a0Personal Healthcare"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3935-8021","authenticated-orcid":false,"given":"Longfei","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6300-5103","authenticated-orcid":false,"given":"Subramanian","family":"Ramamoorthy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6860-9371","authenticated-orcid":false,"given":"Robert B.","family":"Fisher","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,12]]},"reference":[{"issue":"6","key":"13_CR1","doi-asserted-by":"publisher","first-page":"2068","DOI":"10.3390\/s21062068","volume":"21","author":"A Ancans","year":"2021","unstructured":"Ancans, A., Greitans, M., Cacurs, R., Banga, B., Rozentals, A.: Wearable sensor clothing for body movement measurement during physical activities in healthcare. Sensors 21(6), 2068 (2021)","journal-title":"Sensors"},{"key":"13_CR2","doi-asserted-by":"crossref","unstructured":"Andriluka, M., Pishchulin, L., Gehler, P., Schiele, B.: 2D human pose estimation: new benchmark and state of the art analysis. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014)","DOI":"10.1109\/CVPR.2014.471"},{"issue":"1","key":"13_CR3","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0279697","volume":"18","author":"J Bertram","year":"2023","unstructured":"Bertram, J., et al.: Accuracy and repeatability of the Microsoft azure Kinect for clinical measurement of motor function. PLoS ONE 18(1), e0279697 (2023)","journal-title":"PLoS ONE"},{"key":"13_CR4","doi-asserted-by":"publisher","unstructured":"Butler, D.J., Wulff, J., Stanley, G.B., Black, M.J.: A naturalistic open source movie for optical flow evaluation. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7577, pp. 611\u2013625. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-33783-3_44","DOI":"10.1007\/978-3-642-33783-3_44"},{"key":"13_CR5","doi-asserted-by":"crossref","unstructured":"Chen, L., Fisher, R.B.: MISO: monitoring inactivity of single older adults at home using RGB-D technology. ACM Transactions on Computing for Healthcare (2024)","DOI":"10.1145\/3674848"},{"key":"13_CR6","doi-asserted-by":"crossref","unstructured":"Cheng, B., Xiao, B., Wang, J., Shi, H., Huang, T.S., Zhang, L.: HigherHRNet: scale-aware representation learning for bottom-up human pose estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5386\u20135395 (2020)","DOI":"10.1109\/CVPR42600.2020.00543"},{"issue":"7","key":"13_CR7","doi-asserted-by":"publisher","first-page":"173","DOI":"10.3390\/fi13070173","volume":"13","author":"J Chua","year":"2021","unstructured":"Chua, J., Ong, L.Y., Leow, M.C.: Telehealth using PoseNet-based system for in-home rehabilitation. Future Internet 13(7), 173 (2021)","journal-title":"Future Internet"},{"key":"13_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40798-018-0139-y","volume":"4","author":"SL Colyer","year":"2018","unstructured":"Colyer, S.L., Evans, M., Cosker, D.P., Salo, A.I.: A review of the evolution of vision-based motion analysis and the integration of advanced computer vision methods towards developing a markerless system. Sports Med. Open 4, 1\u201315 (2018)","journal-title":"Sports Med. Open"},{"key":"13_CR9","doi-asserted-by":"crossref","unstructured":"Cook, D., Das, S.K.: Smart environments: technology, protocols, and applications, vol.\u00a043. John Wiley & Sons (2004)","DOI":"10.1002\/047168659X"},{"key":"13_CR10","doi-asserted-by":"crossref","unstructured":"Dosovitskiy, A., et al.: FlowNet: learning optical flow with convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2758\u20132766 (2015)","DOI":"10.1109\/ICCV.2015.316"},{"key":"13_CR11","unstructured":"Fan, L., et al.: SURREAL: open-source reinforcement learning framework and robot manipulation benchmark. In: Conference on Robot Learning, pp. 767\u2013782. PMLR (2018)"},{"key":"13_CR12","doi-asserted-by":"publisher","unstructured":"Farneb\u00e4ck, G.: Two-frame motion estimation based on polynomial expansion. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 363\u2013370. Springer, Heidelberg (2003). https:\/\/doi.org\/10.1007\/3-540-45103-X_50","DOI":"10.1007\/3-540-45103-X_50"},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"Francisco, J.A., Rodrigues, P.S.: Computer vision based on a modular neural network for automatic assessment of physical therapy rehabilitation activities. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2022)","DOI":"10.1109\/TNSRE.2022.3226459"},{"issue":"2","key":"13_CR14","doi-asserted-by":"publisher","DOI":"10.2196\/mhealth.3505","volume":"3","author":"EB Hekler","year":"2015","unstructured":"Hekler, E.B., et al.: Validation of physical activity tracking via android smartphones compared to ActiGraph accelerometer: laboratory-based and free-living validation studies. JMIR Mhealth Uhealth 3(2), e3505 (2015)","journal-title":"JMIR Mhealth Uhealth"},{"issue":"1\u20133","key":"13_CR15","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/0004-3702(81)90024-2","volume":"17","author":"BK Horn","year":"1981","unstructured":"Horn, B.K., Schunck, B.G.: Determining optical flow. Artif. Intell. 17(1\u20133), 185\u2013203 (1981)","journal-title":"Artif. Intell."},{"key":"13_CR16","doi-asserted-by":"crossref","unstructured":"Ionescu, C., Papava, D., Olaru, V., Sminchisescu, C.: Human3.6M: large scale datasets and predictive methods for 3D human sensing in natural environments. IEEE Trans. Pattern Anal. Mach. Intell. 36(7), 1325\u20131339 (2014)","DOI":"10.1109\/TPAMI.2013.248"},{"key":"13_CR17","doi-asserted-by":"crossref","unstructured":"Jhuang, H., Gall, J., Zuffi, S., Schmid, C., Black, M.J.: Towards understanding action recognition. In: International Conference on Computer Vision (ICCV), pp. 3192\u20133199 (2013)","DOI":"10.1109\/ICCV.2013.396"},{"key":"13_CR18","unstructured":"Jocher, G., Chaurasia, A., Qiu, J., Milajerdi, A.: YOLOv8: Ultralytics\u2019 next-generation real-time object detection. https:\/\/github.com\/ultralytics\/ultralytics (2023). Accessed 17 Aug 2024"},{"issue":"11","key":"13_CR19","doi-asserted-by":"publisher","first-page":"3771","DOI":"10.3390\/s21113771","volume":"21","author":"A Kashevnik","year":"2021","unstructured":"Kashevnik, A., Othman, W., Ryabchikov, I., Shilov, N.: Estimation of motion and respiratory characteristics during the meditation practice based on video analysis. Sensors 21(11), 3771 (2021)","journal-title":"Sensors"},{"issue":"11","key":"13_CR20","doi-asserted-by":"publisher","first-page":"3312","DOI":"10.3390\/s20113312","volume":"20","author":"MH Khan","year":"2020","unstructured":"Khan, M.H., Z\u00f6ller, M., Farid, M.S., Grzegorzek, M.: Marker-based movement analysis of human body parts in therapeutic procedure. Sensors 20(11), 3312 (2020)","journal-title":"Sensors"},{"key":"13_CR21","doi-asserted-by":"crossref","unstructured":"Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., Serre, T.: HMDB: a large video database for human motion recognition. In: 2011 International Conference on Computer Vision, pp. 2556\u20132563. IEEE (2011)","DOI":"10.1109\/ICCV.2011.6126543"},{"key":"13_CR22","doi-asserted-by":"crossref","unstructured":"Li, Y., Wang, C., Cao, Y., Liu, B., Tan, J., Luo, Y.: Human pose estimation based in-home lower body rehabilitation system. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp.\u00a01\u20138. IEEE (2020)","DOI":"10.1109\/IJCNN48605.2020.9207296"},{"issue":"22","key":"13_CR23","doi-asserted-by":"publisher","first-page":"7821","DOI":"10.1109\/JSEN.2016.2609392","volume":"16","author":"IH Lopez-Nava","year":"2016","unstructured":"Lopez-Nava, I.H., Munoz-Melendez, A.: Wearable inertial sensors for human motion analysis: a review. IEEE Sens. J. 16(22), 7821\u20137834 (2016)","journal-title":"IEEE Sens. J."},{"key":"13_CR24","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1007\/s12652-010-0043-x","volume":"3","author":"A Lotfi","year":"2012","unstructured":"Lotfi, A., Langensiepen, C., Mahmoud, S.M., Akhlaghinia, M.J.: Smart homes for the elderly dementia sufferers: identification and prediction of abnormal behaviour. J. Ambient. Intell. Humaniz. Comput. 3, 205\u2013218 (2012)","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"13_CR25","unstructured":"Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: IJCAI\u201981: 7th International Joint Conference on Artificial Intelligence. vol.\u00a02, pp. 674\u2013679 (1981)"},{"key":"13_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.107016","volume":"225","author":"E Martini","year":"2022","unstructured":"Martini, E., et al.: Enabling gait analysis in the telemedicine practice through portable and accurate 3D human pose estimation. Comput. Methods Programs Biomed. 225, 107016 (2022)","journal-title":"Comput. Methods Programs Biomed."},{"key":"13_CR27","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2022.867485","volume":"16","author":"TE McGuirk","year":"2022","unstructured":"McGuirk, T.E., Perry, E.S., Sihanath, W.B., Riazati, S., Patten, C.: Feasibility of markerless motion capture for three-dimensional gait assessment in community settings. Front. Hum. Neurosci. 16, 867485 (2022)","journal-title":"Front. Hum. Neurosci."},{"key":"13_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1743-0003-3-6","volume":"3","author":"L M\u00fcndermann","year":"2006","unstructured":"M\u00fcndermann, L., Corazza, S., Andriacchi, T.P.: The evolution of methods for the capture of human movement leading to markerless motion capture for biomechanical applications. J. Neuroeng. Rehabil. 3, 1\u201311 (2006)","journal-title":"J. Neuroeng. Rehabil."},{"issue":"2","key":"13_CR29","doi-asserted-by":"publisher","first-page":"3362","DOI":"10.3390\/s140203362","volume":"14","author":"A Muro-De-La-Herran","year":"2014","unstructured":"Muro-De-La-Herran, A., Garcia-Zapirain, B., Mendez-Zorrilla, A.: Gait analysis methods: an overview of wearable and non-wearable systems, highlighting clinical applications. Sensors 14(2), 3362\u20133394 (2014)","journal-title":"Sensors"},{"key":"13_CR30","doi-asserted-by":"publisher","first-page":"50","DOI":"10.3389\/fspor.2020.00050","volume":"2","author":"N Nakano","year":"2020","unstructured":"Nakano, N., et al.: Evaluation of 3D markerless motion capture accuracy using OpenPose with multiple video cameras. Front. Sports Act. Living 2, 50 (2020)","journal-title":"Front. Sports Act. Living"},{"key":"13_CR31","doi-asserted-by":"crossref","unstructured":"Nascimento, L.M.S.d., Bonfati, L.V., Freitas, M.L.B., Mendes\u00a0Junior, J.J.A., Siqueira, H.V., Stevan\u00a0Jr, S.L.: Sensors and systems for physical rehabilitation and health monitoring-a review. Sensors 20(15), 4063 (2020)","DOI":"10.3390\/s20154063"},{"key":"13_CR32","doi-asserted-by":"crossref","unstructured":"Pantelopoulos, A., Bourbakis, N.G.: A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Trans. Syst. Man Cybern. Part C (Applications and Reviews) 40(1), 1\u201312 (2009)","DOI":"10.1109\/TSMCC.2009.2032660"},{"key":"13_CR33","doi-asserted-by":"publisher","first-page":"252","DOI":"10.5201\/ipol.2013.21","volume":"3","author":"JS P\u00e9rez","year":"2013","unstructured":"P\u00e9rez, J.S., L\u00f3pez, N.M., de la Nuez, A.S.: Robust optical flow estimation. Image Process. Line 3, 252\u2013270 (2013)","journal-title":"Image Process. Line"},{"issue":"5","key":"13_CR34","doi-asserted-by":"publisher","first-page":"274","DOI":"10.3109\/03091902.2014.909540","volume":"38","author":"A Pfister","year":"2014","unstructured":"Pfister, A., West, A.M., Bronner, S., Noah, J.A.: Comparative abilities of Microsoft Kinect and Vicon 3D motion capture for gait analysis. J. Med. Eng. Technol. 38(5), 274\u2013280 (2014)","journal-title":"J. Med. Eng. Technol."},{"key":"13_CR35","unstructured":"Ranjan, A., Romero, J., Black, M.J.: Learning human optical flow. arXiv preprint arXiv:1806.05666 (2018)"},{"issue":"5","key":"13_CR36","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1016\/S0167-9457(99)00023-8","volume":"18","author":"JG Richards","year":"1999","unstructured":"Richards, J.G.: The measurement of human motion: a comparison of commercially available systems. Hum. Mov. Sci. 18(5), 589\u2013602 (1999)","journal-title":"Hum. Mov. Sci."},{"issue":"16","key":"13_CR37","doi-asserted-by":"publisher","first-page":"5437","DOI":"10.3390\/s21165437","volume":"21","author":"S Rupprechter","year":"2021","unstructured":"Rupprechter, S., et al.: A clinically interpretable computer-vision based method for quantifying gait in Parkinson\u2019s disease. Sensors 21(16), 5437 (2021)","journal-title":"Sensors"},{"issue":"5","key":"13_CR38","doi-asserted-by":"publisher","first-page":"2288","DOI":"10.1109\/JBHI.2022.3144917","volume":"26","author":"A Sabo","year":"2022","unstructured":"Sabo, A., Mehdizadeh, S., Iaboni, A., Taati, B.: Estimating parkinsonism severity in natural gait videos of older adults with dementia. IEEE J. Biomed. Health Inform. 26(5), 2288\u20132298 (2022)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"13_CR39","doi-asserted-by":"crossref","unstructured":"Scano, A., Caimmi, M., Malosio, M., Tosatti, L.M.: Using Kinect for upper-limb functional evaluation in home rehabilitation: a comparison with a 3D stereoscopic passive marker system. In: 5th IEEE RAS\/EMBS International Conference on Biomedical Robotics and Biomechatronics, pp. 561\u2013566. IEEE (2014)","DOI":"10.1109\/BIOROB.2014.6913837"},{"key":"13_CR40","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.13517","volume":"10","author":"B Scott","year":"2022","unstructured":"Scott, B., Seyres, M., Philp, F., Chadwick, E.K., Blana, D.: Healthcare applications of single camera markerless motion capture: a scoping review. PeerJ 10, e13517 (2022)","journal-title":"PeerJ"},{"issue":"3","key":"13_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42452-021-04227-x","volume":"3","author":"S Shah","year":"2021","unstructured":"Shah, S., Xuezhi, X.: Traditional and modern strategies for optical flow: an investigation. SN Appl. Sci. 3(3), 1\u201314 (2021). https:\/\/doi.org\/10.1007\/s42452-021-04227-x","journal-title":"SN Appl. Sci."},{"issue":"9","key":"13_CR42","doi-asserted-by":"publisher","first-page":"12694","DOI":"10.3390\/s120912694","volume":"12","author":"N Sharmin","year":"2012","unstructured":"Sharmin, N., Brad, R.: Optimal filter estimation for Lucas-Kanade optical flow. Sensors 12(9), 12694\u201312709 (2012)","journal-title":"Sensors"},{"issue":"4","key":"13_CR43","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1008935","volume":"17","author":"J Stenum","year":"2021","unstructured":"Stenum, J., Rossi, C., Roemmich, R.T.: Two-dimensional video-based analysis of human gait using pose estimation. PLoS Comput. Biol. 17(4), e1008935 (2021)","journal-title":"PLoS Comput. Biol."},{"key":"13_CR44","doi-asserted-by":"crossref","unstructured":"Sun, D., Yang, X., Liu, M.Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8934\u20138943 (2018)","DOI":"10.1109\/CVPR.2018.00931"},{"key":"13_CR45","doi-asserted-by":"crossref","unstructured":"Suzuki, S., Amemiya, Y., Sato, M.: Deep learning assessment of child gross-motor. In: 2020 13th International Conference on Human System Interaction (HSI), pp. 189\u2013194. IEEE (2020)","DOI":"10.1109\/HSI49210.2020.9142684"},{"key":"13_CR46","doi-asserted-by":"publisher","unstructured":"Teed, Z., Deng, J.: RAFT: recurrent all-pairs field transforms for optical flow. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12347, pp. 402\u2013419. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58536-5_24","DOI":"10.1007\/978-3-030-58536-5_24"},{"key":"13_CR47","doi-asserted-by":"crossref","unstructured":"Toshev, A., Szegedy, C.: DeepPose: human pose estimation via deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1653\u20131660 (2014)","DOI":"10.1109\/CVPR.2014.214"},{"issue":"12","key":"13_CR48","doi-asserted-by":"publisher","first-page":"2776","DOI":"10.1016\/j.jbiomech.2008.06.024","volume":"41","author":"M Windolf","year":"2008","unstructured":"Windolf, M., G\u00f6tzen, N., Morlock, M.: Systematic accuracy and precision analysis of video motion capturing systems-exemplified on the Vicon-460 system. J. Biomech. 41(12), 2776\u20132780 (2008)","journal-title":"J. Biomech."},{"key":"13_CR49","doi-asserted-by":"publisher","unstructured":"Xiao, J., Cheng, H., Sawhney, H., Rao, C., Isnardi, M.: Bilateral filtering-based optical flow estimation with occlusion detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 211\u2013224. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11744023_17","DOI":"10.1007\/11744023_17"},{"key":"13_CR50","first-page":"38571","volume":"35","author":"Y Xu","year":"2022","unstructured":"Xu, Y., Zhang, J., Zhang, Q., Tao, D.: VITPose: simple vision transformer baselines for human pose estimation. Adv. Neural. Inf. Process. Syst. 35, 38571\u201338584 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"8","key":"13_CR51","doi-asserted-by":"publisher","first-page":"N63","DOI":"10.1088\/0967-3334\/34\/8\/N63","volume":"34","author":"JT Zhang","year":"2013","unstructured":"Zhang, J.T., Novak, A.C., Brouwer, B., Li, Q.: Concurrent validation of Xsens MVN measurement of lower limb joint angular kinematics. Physiol. Meas. 34(8), N63 (2013)","journal-title":"Physiol. Meas."},{"issue":"3","key":"13_CR52","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1007\/s11263-012-0549-0","volume":"101","author":"S Zuffi","year":"2013","unstructured":"Zuffi, S., Black, M.J.: Puppet flow. IJCV 101(3), 437\u2013458 (2013)","journal-title":"IJCV"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-92591-7_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T07:24:48Z","timestamp":1747985088000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-92591-7_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031925900","9783031925917"],"references-count":52,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-92591-7_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"12 May 2025","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":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}