{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T20:55:37Z","timestamp":1742936137408,"version":"3.40.3"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031431470"},{"type":"electronic","value":"9783031431487"}],"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\/978-3-031-43148-7_27","type":"book-chapter","created":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T20:48:35Z","timestamp":1693860515000},"page":"316-327","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MC-GTA: A Synthetic Benchmark for\u00a0Multi-Camera Vehicle Tracking"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6985-0439","authenticated-orcid":false,"given":"Luca","family":"Ciampi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3011-2487","authenticated-orcid":false,"given":"Nicola","family":"Messina","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gaetano Emanuele","family":"Valenti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0171-4315","authenticated-orcid":false,"given":"Giuseppe","family":"Amato","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6258-5313","authenticated-orcid":false,"given":"Fabrizio","family":"Falchi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3715-149X","authenticated-orcid":false,"given":"Claudio","family":"Gennaro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,5]]},"reference":[{"key":"27_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1007\/978-3-030-30642-7_27","volume-title":"Image Analysis and Processing \u2013 ICIAP 2019","author":"G Amato","year":"2019","unstructured":"Amato, G., Ciampi, L., Falchi, F., Gennaro, C., Messina, N.: Learning pedestrian detection from virtual worlds. In: Ricci, E., Rota Bul\u00f2, S., Snoek, C., Lanz, O., Messelodi, S., Sebe, N. (eds.) ICIAP 2019. LNCS, vol. 11751, pp. 302\u2013312. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-30642-7_27"},{"key":"27_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117125","volume":"199","author":"MD Benedetto","year":"2022","unstructured":"Benedetto, M.D., Carrara, F., Ciampi, L., Falchi, F., Gennaro, C., Amato, G.: An embedded toolset for human activity monitoring in critical environments. Expert Syst. Appl. 199, 117125 (2022). https:\/\/doi.org\/10.1016\/j.eswa.2022.117125","journal-title":"Expert Syst. Appl."},{"key":"27_CR3","doi-asserted-by":"publisher","unstructured":"Bewley, A., Ge, Z., Ott, L., Ramos, F., Upcroft, B.: Simple online and realtime tracking. In: 2016 IEEE International Conference on Image Processing (ICIP). IEEE, September 2016. https:\/\/doi.org\/10.1109\/icip.2016.7533003","DOI":"10.1109\/icip.2016.7533003"},{"key":"27_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/978-3-030-58452-8_13","volume-title":"Computer Vision \u2013 ECCV 2020","author":"N Carion","year":"2020","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 213\u2013229. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_13"},{"key":"27_CR5","doi-asserted-by":"publisher","unstructured":"Carrara, F., Pasco, L., Gennaro, C., Falchi, F.: Learning to detect fallen people in virtual worlds. In: International Conference on Content-based Multimedia Indexing. ACM, September 2022. https:\/\/doi.org\/10.1145\/3549555.3549573","DOI":"10.1145\/3549555.3549573"},{"issue":"18","key":"27_CR6","doi-asserted-by":"publisher","first-page":"5250","DOI":"10.3390\/s20185250","volume":"20","author":"L Ciampi","year":"2020","unstructured":"Ciampi, L., Messina, N., Falchi, F., Gennaro, C., Amato, G.: Virtual to real adaptation of pedestrian detectors. Sensors 20(18), 5250 (2020). https:\/\/doi.org\/10.3390\/s20185250","journal-title":"Sensors"},{"key":"27_CR7","doi-asserted-by":"publisher","unstructured":"Ciampi., L., Santiago., C., Costeira., J., Falchi., F., Gennaro., C., Amato., G.: Unsupervised domain adaptation for video violence detection in the wild. In: Proceedings of the 3rd International Conference on Image Processing and Vision Engineering - IMPROVE, pp. 37\u201346. INSTICC, SciTePress (2023). https:\/\/doi.org\/10.5220\/0011965300003497","DOI":"10.5220\/0011965300003497"},{"key":"27_CR8","doi-asserted-by":"publisher","unstructured":"Ciampi, L., Santiago, C., Costeira, J., Gennaro, C., Amato, G.: Domain adaptation for traffic density estimation. In: Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. SCITEPRESS - Science and Technology Publications (2021). https:\/\/doi.org\/10.5220\/0010303401850195","DOI":"10.5220\/0010303401850195"},{"key":"27_CR9","unstructured":"Deschaud, J.: KITTI-CARLA: a kitti-like dataset generated by CARLA simulator. CoRR abs\/2109.00892 (2021)"},{"key":"27_CR10","unstructured":"Dosovitskiy, A., Ros, G., Codevilla, F., L\u00f3pez, A.M., Koltun, V.: CARLA: an open urban driving simulator. In: 1st Annual Conference on Robot Learning, CoRL 2017, Mountain View, California, USA, November 13\u201315, 2017, Proceedings. Proceedings of Machine Learning Research, vol. 78, pp. 1\u201316. PMLR (2017)"},{"key":"27_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1007\/978-3-030-01225-0_27","volume-title":"Computer Vision \u2013 ECCV 2018","author":"M Fabbri","year":"2018","unstructured":"Fabbri, M., Lanzi, F., Calderara, S., Palazzi, A., Vezzani, R., Cucchiara, R.: Learning to detect and track visible and occluded body joints in a virtual world. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11208, pp. 450\u2013466. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01225-0_27"},{"key":"27_CR12","doi-asserted-by":"publisher","unstructured":"Foszner, P., et al.: CrowdSim2: an open synthetic benchmark for object detectors. In: Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. SCITEPRESS - Science and Technology Publications (2023). https:\/\/doi.org\/10.5220\/0011692500003417","DOI":"10.5220\/0011692500003417"},{"key":"27_CR13","doi-asserted-by":"publisher","unstructured":"Foszner, P., et al.: Development of a realistic crowd simulation environment for fine-grained validation of people tracking methods. In: Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. SCITEPRESS - Science and Technology Publications (2023). https:\/\/doi.org\/10.5220\/0011691500003417","DOI":"10.5220\/0011691500003417"},{"key":"27_CR14","unstructured":"Ge, Z., Liu, S., Wang, F., Li, Z., Sun, J.: YOLOX: exceeding YOLO series in 2021. arXiv preprint arXiv:2107.08430 (2021)"},{"key":"27_CR15","doi-asserted-by":"publisher","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.B.: Mask R-CNN. In: IEEE International Conference on Computer Vision, ICCV 2017, pp. 2980\u20132988. IEEE Computer Society (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.322","DOI":"10.1109\/ICCV.2017.322"},{"key":"27_CR16","doi-asserted-by":"publisher","unstructured":"Jocher, G., et al.: ultralytics\/yolov5: v7.0 - YOLOv5 SOTA Realtime Instance Segmentation, November 2022. https:\/\/doi.org\/10.5281\/zenodo.7347926","DOI":"10.5281\/zenodo.7347926"},{"key":"27_CR17","doi-asserted-by":"publisher","unstructured":"Kohl, P., Specker, A., Schumann, A., Beyerer, J.: The MTA dataset for multi target multi camera pedestrian tracking by weighted distance aggregation. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, June 2020. https:\/\/doi.org\/10.1109\/cvprw50498.2020.00529","DOI":"10.1109\/cvprw50498.2020.00529"},{"key":"27_CR18","doi-asserted-by":"publisher","unstructured":"Li, Y., Hilton, A., Illingworth, J.: Towards reliable real-time multiview tracking. In: Proceedings 2001 IEEE Workshop on Multi-Object Tracking. IEEE Computer Society. https:\/\/doi.org\/10.1109\/mot.2001.937980","DOI":"10.1109\/mot.2001.937980"},{"issue":"2","key":"27_CR19","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","volume":"42","author":"T Lin","year":"2020","unstructured":"Lin, T., Goyal, P., Girshick, R.B., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. IEEE Trans. Pattern Anal. Mach. Intell. 42(2), 318\u2013327 (2020). https:\/\/doi.org\/10.1109\/TPAMI.2018.2858826","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"27_CR20","doi-asserted-by":"publisher","unstructured":"Liu, C., et al.: City-scale multi-camera vehicle tracking guided by crossroad zones. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, June 2021. https:\/\/doi.org\/10.1109\/cvprw53098.2021.00466","DOI":"10.1109\/cvprw53098.2021.00466"},{"key":"27_CR21","doi-asserted-by":"publisher","unstructured":"Liu, H., Tian, Y., Wang, Y., Pang, L., Huang, T.: Deep relative distance learning: tell the difference between similar vehicles. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, June 2016. https:\/\/doi.org\/10.1109\/cvpr.2016.238","DOI":"10.1109\/cvpr.2016.238"},{"key":"27_CR22","doi-asserted-by":"publisher","unstructured":"Liu, X., Liu, W., Mei, T., Ma, H.: PROVID: progressive and multimodal vehicle reidentification for large-scale urban surveillance. IEEE Trans. Multimed. 20(3), 645\u2013658 (2018). https:\/\/doi.org\/10.1109\/tmm.2017.2751966","DOI":"10.1109\/tmm.2017.2751966"},{"key":"27_CR23","doi-asserted-by":"publisher","unstructured":"Meinhardt, T., Kirillov, A., Leal-Taixe, L., Feichtenhofer, C.: TrackFormer: multi-object tracking with transformers. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, June 2022. https:\/\/doi.org\/10.1109\/cvpr52688.2022.00864","DOI":"10.1109\/cvpr52688.2022.00864"},{"key":"27_CR24","doi-asserted-by":"crossref","unstructured":"Qian, Y., Yu, L., Liu, W., Hauptmann, A.G.: Electricity: an efficient multi-camera vehicle tracking system for intelligent city. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 588\u2013589 (2020)","DOI":"10.1109\/CVPRW50498.2020.00302"},{"key":"27_CR25","unstructured":"Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)"},{"key":"27_CR26","doi-asserted-by":"publisher","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster r-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137\u20131149 (2017). https:\/\/doi.org\/10.1109\/tpami.2016.2577031","DOI":"10.1109\/tpami.2016.2577031"},{"key":"27_CR27","doi-asserted-by":"publisher","unstructured":"Staniszewski, M., et al.: Application of crowd simulations in the evaluation of tracking algorithms. Sensors. 20(17), 4960 (2020). https:\/\/doi.org\/10.3390\/s20174960","DOI":"10.3390\/s20174960"},{"key":"27_CR28","unstructured":"Tan, X., et al.: Multi-camera vehicle tracking and re-identification based on visual and spatial-temporal features. In: CVPR Workshops, pp. 275\u2013284 (2019)"},{"key":"27_CR29","doi-asserted-by":"crossref","unstructured":"Wang, C., Bochkovskiy, A., Liao, H.M.: Yolov7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. CoRR abs\/2207.02696 (2022). arXiv:2207.02696","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"27_CR30","doi-asserted-by":"publisher","unstructured":"Wojke, N., Bewley, A., Paulus, D.: Simple online and realtime tracking with a deep association metric. In: 2017 IEEE International Conference on Image Processing (ICIP). IEEE, September 2017. https:\/\/doi.org\/10.1109\/icip.2017.8296962","DOI":"10.1109\/icip.2017.8296962"},{"key":"27_CR31","doi-asserted-by":"publisher","unstructured":"Zhang, Y., et al.: ByteTrack: multi-object tracking by associating every detection box. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds) Computer Vision \u2013 ECCV 2022. LNCS, vol. 13682, pp. 1\u201321. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-20047-2_1","DOI":"10.1007\/978-3-031-20047-2_1"},{"key":"27_CR32","unstructured":"Zhou, X., Wang, D., Kr\u00e4henb\u00fchl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019)"},{"key":"27_CR33","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable DETR: deformable transformers for end-to-end object detection. In: 9th International Conference on Learning Representations, ICLR 2021. OpenReview.net (2021)"}],"container-title":["Lecture Notes in Computer Science","Image Analysis and Processing \u2013 ICIAP 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43148-7_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T16:39:18Z","timestamp":1710261558000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43148-7_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031431470","9783031431487"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43148-7_27","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":"5 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIAP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image Analysis and Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Udine","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iciap2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iciap2023.org\/","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":"144","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":"85","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":"7","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":"59% - 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":"3","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":"https:\/\/iciap2023.org\/satellite-event\/workshops\/","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)"}}]}}