{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T15:13:34Z","timestamp":1768662814763,"version":"3.49.0"},"publisher-location":"Cham","reference-count":71,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032144942","type":"print"},{"value":"9783032144959","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-14495-9_21","type":"book-chapter","created":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T15:22:59Z","timestamp":1768576979000},"page":"272-285","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SLICE Street-Level Insights from\u00a0Camera Evidence"],"prefix":"10.1007","author":[{"given":"Peter","family":"Roch","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lukas","family":"Laaser","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bijan","family":"Shahbaz Nejad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marcus","family":"Handte","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pedro Jos\u00e9","family":"Marr\u00f3n","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,17]]},"reference":[{"key":"21_CR1","unstructured":"American Transportation Research Institute (ATRI): Cost of congestion to the trucking industry (2023). https:\/\/truckingresearch.org\/wp-content\/uploads\/2023\/10\/ATRI_CostOfCongestion2023.pdf. Accessed 28 July 2025"},{"key":"21_CR2","doi-asserted-by":"crossref","unstructured":"Azhar, M.I.H., Zaman, F.H.K., Tahir, N.M., Hashim, H.: People tracking system using DeepSORT. In: ICCSCE (2020)","DOI":"10.1109\/ICCSCE50387.2020.9204956"},{"key":"21_CR3","doi-asserted-by":"crossref","unstructured":"Bahnsen, C.H., Moeslund, T.B.: Rain removal in traffic surveillance: does it matter? T-ITS (2019)","DOI":"10.1109\/TITS.2018.2872502"},{"key":"21_CR4","doi-asserted-by":"crossref","unstructured":"Bharati, P., Pramanik, A.: Deep learning techniques\u2014R-CNN to mask R-CNN: a survey. In: CIPR (2020)","DOI":"10.1007\/978-981-13-9042-5_56"},{"key":"21_CR5","doi-asserted-by":"crossref","unstructured":"Bochinski, E., Senst, T., Sikora, T.: Extending IOU based multi-object tracking by visual information. In: AVSS (2018)","DOI":"10.1109\/AVSS.2018.8639144"},{"key":"21_CR6","unstructured":"California Department of Transportation: California department of transportation conditions of use (2025). https:\/\/dot.ca.gov\/conditions-of-use. Accessed 10 Oct 2025"},{"key":"21_CR7","unstructured":"California Department of Transportation: Caltrans :\u00a0: Commercial wholesale web portal :\u00a0: Cameras (CCTV) (2025). https:\/\/cwwp2.dot.ca.gov\/documentation\/cctv\/cctv.htm. Accessed 10 Oct 2025"},{"key":"21_CR8","unstructured":"CAS Software AG: Moby dex (2025). https:\/\/mobydex.org\/. Accessed 07 Aug 2025"},{"key":"21_CR9","doi-asserted-by":"crossref","unstructured":"Chen, F., Wang, X., Zhao, Y., Lv, S., Niu, X.: Visual object tracking: a survey. CVIU (2022)","DOI":"10.1016\/j.cviu.2022.103508"},{"key":"21_CR10","doi-asserted-by":"crossref","unstructured":"Cheng, H.Y.: Highway traffic flow estimation for surveillance scenes damaged by rain. IEEE Intell. Syst. (2018)","DOI":"10.1109\/MIS.2018.111144331"},{"key":"21_CR11","doi-asserted-by":"crossref","unstructured":"Cheng, Q., Li, H., Yang, Y., Ling, J., Huang, X.: Towards efficient risky driving detection: a benchmark and a semi-supervised model. Sensors (2024)","DOI":"10.3390\/s24051386"},{"key":"21_CR12","doi-asserted-by":"crossref","unstructured":"Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.350"},{"key":"21_CR13","doi-asserted-by":"crossref","unstructured":"Costa, L.G., Cole, T.B., Coburn, J., Chang, Y.C., Dao, K., Roqu\u00e9, P.J.: Neurotoxicity of traffic-related air pollution. NeuroToxicology (2017)","DOI":"10.1016\/j.neuro.2015.11.008"},{"key":"21_CR14","unstructured":"Creative Commons: Creative commons attribution 4.0 international public license (2013). https:\/\/creativecommons.org\/licenses\/by\/4.0\/. Accessed 13 Oct 2025"},{"key":"21_CR15","doi-asserted-by":"crossref","unstructured":"Dailey, D., Cathey, F., Pumrin, S.: An algorithm to estimate mean traffic speed using uncalibrated cameras. T-ITS (2000)","DOI":"10.1109\/6979.880967"},{"key":"21_CR16","doi-asserted-by":"crossref","unstructured":"Dave, A., Khurana, T., Tokmakov, P., Schmid, C., Ramanan, D.: TAO: a large-scale benchmark for tracking any object. In: ECCV (2020)","DOI":"10.1007\/978-3-030-58558-7_26"},{"key":"21_CR17","doi-asserted-by":"crossref","unstructured":"Dong, Z., Wu, Y., Pei, M., Jia, Y.: Vehicle type classification using a semisupervised convolutional neural network. T-ITS (2015)","DOI":"10.1109\/TITS.2015.2402438"},{"key":"21_CR18","doi-asserted-by":"crossref","unstructured":"Everingham, M., Eslami, S.M.A., Van\u00a0Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes challenge: a retrospective. IJCV (2015)","DOI":"10.1007\/s11263-014-0733-5"},{"key":"21_CR19","doi-asserted-by":"crossref","unstructured":"Farneb\u00e4ck, G.: Two-frame motion estimation based on polynomial expansion. In: Image Analysis (2003)","DOI":"10.1007\/3-540-45103-X_50"},{"key":"21_CR20","doi-asserted-by":"crossref","unstructured":"Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. J. Big Data (2019)","DOI":"10.1186\/s40537-019-0234-z"},{"key":"21_CR21","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the KITTI dataset. Int. J. Robot (2013)","DOI":"10.1177\/0278364913491297"},{"key":"21_CR22","doi-asserted-by":"crossref","unstructured":"Ghahremannezhad, H., Shi, H., Liu, C.: Real-time accident detection in traffic surveillance using deep learning. In: IST (2022)","DOI":"10.1109\/IST55454.2022.9827736"},{"key":"21_CR23","doi-asserted-by":"crossref","unstructured":"Gruber, T., Julca-Aguilar, F., Bijelic, M., Heide, F.: Gated2Depth: real-time dense Lidar from gated images. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00159"},{"key":"21_CR24","doi-asserted-by":"crossref","unstructured":"Guerrero-G\u00f3mez-Olmedo, R., L\u00f3pez-Sastre, R.J., Maldonado-Basc\u00f3n, S., Fern\u00e1ndez-Caballero, A.: Vehicle tracking by simultaneous detection and viewpoint estimation. In: IWINAC (2013)","DOI":"10.1007\/978-3-642-38622-0_32"},{"key":"21_CR25","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. TPAMI (2020)","DOI":"10.1109\/TPAMI.2018.2844175"},{"key":"21_CR26","doi-asserted-by":"crossref","unstructured":"Hmidani, O., Ismaili\u00a0Alaoui, E.M.: A comprehensive survey of the R-CNN family for object detection. In: CommNet (2022)","DOI":"10.1109\/CommNet56067.2022.9993862"},{"key":"21_CR27","doi-asserted-by":"crossref","unstructured":"Hou, X., Wang, Y., Chau, L.P.: Vehicle tracking using deep SORT with low confidence track filtering. In: AVSS (2019)","DOI":"10.1109\/AVSS.2019.8909903"},{"key":"21_CR28","doi-asserted-by":"crossref","unstructured":"Hussain, K.F., Moussa, G.S.: On-road vehicle classification based on random neural network and bag-of-visual words. Probab. Eng. Inf. Sci. (2016)","DOI":"10.1017\/S0269964816000073"},{"key":"21_CR29","doi-asserted-by":"crossref","unstructured":"Id\u00e9, T., Katsuki, T., Morimura, T., Morris, R.: City-wide traffic flow estimation from a limited number of low-quality cameras. T-ITS (2017)","DOI":"10.1109\/TITS.2016.2597160"},{"key":"21_CR30","doi-asserted-by":"crossref","unstructured":"Ijjina, E.P., Chand, D., Gupta, S., Goutham, K.: Computer vision-based accident detection in traffic surveillance. In: ICCNT (2019)","DOI":"10.1109\/ICCCNT45670.2019.8944469"},{"key":"21_CR31","unstructured":"Jocher, G., Qiu, J.: Ultralytics YOLO11 (2024). https:\/\/github.com\/ultralytics\/ultralytics. Accessed 17 Oct 2025"},{"key":"21_CR32","doi-asserted-by":"crossref","unstructured":"Kalsotra, R., Arora, S.: A comprehensive survey of video datasets for background subtraction. IEEE Access (2019)","DOI":"10.1109\/ACCESS.2019.2914961"},{"key":"21_CR33","doi-asserted-by":"publisher","unstructured":"Kuznetsova, A., et al.: The open images dataset V4. Int. J. Comput. Vis. 128(7), 1956\u20131981 (2020). https:\/\/doi.org\/10.1007\/s11263-020-01316-z","DOI":"10.1007\/s11263-020-01316-z"},{"key":"21_CR34","doi-asserted-by":"crossref","unstructured":"Li, X., et al.: Dynamic quantification of on-road emissions in Hong Kong: impact from traffic congestion and fleet composition variation. Atmos. Environ. (2023)","DOI":"10.1016\/j.atmosenv.2023.120059"},{"key":"21_CR35","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., et al.: Microsoft COCO: common objects in context. In: ECCV (2014)","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"21_CR36","doi-asserted-by":"crossref","unstructured":"Liu, H., et al.: Video super-resolution based on deep learning: a comprehensive survey. Artif. Intell. Rev. (2022)","DOI":"10.1007\/s10462-022-10147-y"},{"key":"21_CR37","doi-asserted-by":"crossref","unstructured":"Luvizon, D.C., Nassu, B.T., Minetto, R.: A video-based system for vehicle speed measurement in urban roadways. T-ITS (2017)","DOI":"10.1109\/TITS.2016.2606369"},{"key":"21_CR38","doi-asserted-by":"crossref","unstructured":"Maddern, W., Pascoe, G., Linegar, C., Newman, P.: 1 year, 1000 km: the Oxford RobotCar dataset. Int. J. Robot (2017)","DOI":"10.1177\/0278364916679498"},{"key":"21_CR39","doi-asserted-by":"crossref","unstructured":"Maity, M., Banerjee, S., Sinha\u00a0Chaudhuri, S.: Faster R-CNN and YOLO based vehicle detection: a survey. In: ICCMC (2021)","DOI":"10.1109\/ICCMC51019.2021.9418274"},{"key":"21_CR40","doi-asserted-by":"crossref","unstructured":"Marvasti-Zadeh, S.M., Khaghani, J., Ghanei-Yakhdan, H., Kasaei, S., Cheng, L.: COMET: context-aware IoU-guided network for small object tracking. In: ACCV (2021)","DOI":"10.1007\/978-3-030-69532-3_36"},{"key":"21_CR41","doi-asserted-by":"crossref","unstructured":"Mehboob, F., Abbas, M., Almotaeryi, R., Jiang, R., Al-Maadeed, S., Bouridane, A.: Traffic flow estimation from road surveillance. In: ISM (2015)","DOI":"10.1109\/ISM.2015.14"},{"key":"21_CR42","doi-asserted-by":"crossref","unstructured":"Mithun, N.C., Rashid, N.U., Rahman, S.M.M.: Detection and classification of vehicles from video using multiple time-spatial images. T-ITS (2012)","DOI":"10.1109\/TITS.2012.2186128"},{"key":"21_CR43","doi-asserted-by":"crossref","unstructured":"M\u00fcller, M., Bibi, A., Giancola, S., Alsubaihi, S., Ghanem, B.: TrackingNet: a large-scale dataset and benchmark for object tracking in the wild. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01246-5_19"},{"key":"21_CR44","doi-asserted-by":"crossref","unstructured":"Nadrian, H., et al.: Public health impacts of urban traffic jam in Sanandaj, Iran: a case study with mixed-method design. J. Transp. Health (2020)","DOI":"10.1016\/j.jth.2020.100923"},{"key":"21_CR45","doi-asserted-by":"crossref","unstructured":"Nadrian, H., Taghdisi, M.H., Pouyesh, K., Khazaee-Pool, M., Babazadeh, T.: \u201cI am sick and tired of this congestion\u201d: perceptions of Sanandaj inhabitants on the family mental health impacts of urban traffic jam. J. Transp. Health (2019)","DOI":"10.1016\/j.jth.2019.100587"},{"key":"21_CR46","doi-asserted-by":"crossref","unstructured":"Peppa, M.V., Bell, D., Komar, T., Xiao, W.: Urban traffic flow analysis based on deep learning car detection from CCTV image series. ISPRS Arch. (2018)","DOI":"10.5194\/isprs-archives-XLII-4-499-2018"},{"key":"21_CR47","doi-asserted-by":"crossref","unstructured":"Pradeep\u00a0Kumar, P., Kant, K.: TU-DAT: a computer vision dataset on road traffic anomalies. Sensors (2025)","DOI":"10.20944\/preprints202504.2344.v1"},{"key":"21_CR48","doi-asserted-by":"crossref","unstructured":"Qiu, W., Zhong, F., Zhang, Y., Qiao, S., Xiao, Z., Kim, T.S., Wang, Y.: UnrealCV: virtual worlds for computer vision. In: ACM-MM (2017)","DOI":"10.1145\/3123266.3129396"},{"key":"21_CR49","doi-asserted-by":"crossref","unstructured":"Ramachandra, B., Jones, M.J.: Street scene: a new dataset and evaluation protocol for video anomaly detection. In: WACV (2020)","DOI":"10.1109\/WACV45572.2020.9093457"},{"key":"21_CR50","unstructured":"Ramachandra, B., Jones, M.J., Vatsavai, R.R.: A survey of single-scene video anomaly detection. TPAMI (2022)"},{"key":"21_CR51","doi-asserted-by":"crossref","unstructured":"Roch, P., Shahbaz\u00a0Nejad, B., Handte, M., Marr\u00f3n, P.J.: GUILD - a generator for usable images in large-scale datasets. In: ISVC (2022)","DOI":"10.1007\/978-3-031-20716-7_19"},{"key":"21_CR52","doi-asserted-by":"crossref","unstructured":"Shah, A.P., Lamare, J.B., Nguyen-Anh, T., Hauptmann, A.: CADP: a novel dataset for CCTV traffic camera based accident analysis. In: AVSS (2018)","DOI":"10.1109\/AVSS.2018.8639160"},{"key":"21_CR53","unstructured":"Snyder, C., Do, M.: Streets: a novel camera network dataset for traffic flow. In: Adv. Neural Inf. Process. (2019)"},{"key":"21_CR54","doi-asserted-by":"crossref","unstructured":"Sochor, J., et al.: Comprehensive data set for automatic single camera visual speed measurement. T-ITS (2019)","DOI":"10.1109\/TITS.2018.2825609"},{"key":"21_CR55","doi-asserted-by":"crossref","unstructured":"Strigel, E., Meissner, D., Seeliger, F., Wilking, B., Dietmayer, K.: The Ko-PER intersection laserscanner and video dataset. In: ITSC (2014)","DOI":"10.1109\/ITSC.2014.6957976"},{"key":"21_CR56","doi-asserted-by":"crossref","unstructured":"Tripathi, A., Gupta, M.K., Srivastava, C., Dixit, P., Pandey, S.K.: Object detection using YOLO: a survey. In: IC3I (2022)","DOI":"10.1109\/IC3I56241.2022.10073281"},{"key":"21_CR57","unstructured":"Ultralytics Inc.: Configuration (2025). https:\/\/docs.ultralytics.com\/usage\/cfg\/. Accessed 10 Oct 2025"},{"key":"21_CR58","unstructured":"Ultralytics Inc.: Data augmentation using ultralytics YOLO (2025). https:\/\/docs.ultralytics.com\/guides\/yolo-data-augmentation\/. Accessed 10 Oct 2025"},{"key":"21_CR59","unstructured":"Ultralytics Inc.: Ultralytics YOLO hyperparameter tuning guide (2025). https:\/\/docs.ultralytics.com\/guides\/hyperparameter-tuning\/. Accessed 10 Oct 2025"},{"key":"21_CR60","doi-asserted-by":"crossref","unstructured":"Vacavant, A., Chateau, T., Wilhelm, A., Lequi\u00e8vre, L.: A benchmark dataset for outdoor foreground\/background extraction. In: ACCV Workshops (2013)","DOI":"10.1007\/978-3-642-37410-4_25"},{"key":"21_CR61","doi-asserted-by":"crossref","unstructured":"Vijayakumar, A., Vairavasundaram, S.: YOLO-based object detection models: a review and its applications. Multimed. Tools Appl. (2024)","DOI":"10.1007\/s11042-024-18872-y"},{"key":"21_CR62","doi-asserted-by":"crossref","unstructured":"Yang, X., Xiang, W., Zeng, H., Zhang, L.: Real-world video super-resolution: a benchmark dataset and a decomposition based learning scheme. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00474"},{"key":"21_CR63","doi-asserted-by":"crossref","unstructured":"Yu, Z., Jiang, Q., Li, X.: MPP: a novel algorithm for estimating vehicle space headways from a single image. J. Adv. Transp. (2020)","DOI":"10.1155\/2020\/5715686"},{"key":"21_CR64","doi-asserted-by":"crossref","unstructured":"Zahid, A., Qasim, T., Bhatti, N., Zia, M.: A data-driven approach for road accident detection in surveillance videos. Multimed. Tools Appl. (2024)","DOI":"10.1007\/s11042-023-16193-0"},{"key":"21_CR65","doi-asserted-by":"crossref","unstructured":"Zhang, K., Batterman, S.: Air pollution and health risks due to vehicle traffic. Sci. Total. Environ. (2013)","DOI":"10.1016\/j.scitotenv.2013.01.074"},{"key":"21_CR66","doi-asserted-by":"crossref","unstructured":"Zhang, K., Batterman, S., Dion, F.: Vehicle emissions in congestion: comparison of work zone, rush hour and free-flow conditions. Atmos. Environ. (2011)","DOI":"10.1016\/j.atmosenv.2011.01.030"},{"key":"21_CR67","doi-asserted-by":"crossref","unstructured":"Zhang, X., Feng, Y., Angeloudis, P., Demiris, Y.: Monocular visual traffic surveillance: a review. T-ITS (2022)","DOI":"10.1109\/TITS.2022.3147770"},{"key":"21_CR68","doi-asserted-by":"crossref","unstructured":"Zhang, Y., et al.: ByteTrack: multi-object tracking by associating every detection box. In: ECCV (2022)","DOI":"10.1007\/978-3-031-20047-2_1"},{"key":"21_CR69","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Lu, X., Cao, G., Yang, Y., Jiao, L., Liu, F.: ViT-YOLO: transformer-based YOLO for object detection. In: ICCV Workshops (2021)","DOI":"10.1109\/ICCVW54120.2021.00314"},{"key":"21_CR70","doi-asserted-by":"crossref","unstructured":"Zivkovic, Z.: Improved adaptive Gaussian mixture model for background subtraction. In: ICPR (2004)","DOI":"10.1109\/ICPR.2004.1333992"},{"key":"21_CR71","doi-asserted-by":"crossref","unstructured":"Zivkovic, Z., van\u00a0der Heijden, F.: Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recogn. Lett. (2006)","DOI":"10.1016\/j.patrec.2005.11.005"}],"container-title":["Lecture Notes in Computer Science","Advances in Visual Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-14495-9_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T15:23:10Z","timestamp":1768576990000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-14495-9_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032144942","9783032144959"],"references-count":71,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-14495-9_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"17 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ISVC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Visual Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Las Vegas, NV","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 November 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isvc2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.isvc.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}