{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T09:15:34Z","timestamp":1743153334311,"version":"3.40.3"},"publisher-location":"Cham","reference-count":44,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031262920"},{"type":"electronic","value":"9783031262937"}],"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\/978-3-031-26293-7_29","type":"book-chapter","created":{"date-parts":[[2023,3,10]],"date-time":"2023-03-10T20:02:47Z","timestamp":1678478567000},"page":"485-500","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Group Guided Data Association for\u00a0Multiple Object Tracking"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8504-7662","authenticated-orcid":false,"given":"Yubin","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2811-8962","authenticated-orcid":false,"given":"Hao","family":"Sheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1570-6570","authenticated-orcid":false,"given":"Shuai","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9698-9551","authenticated-orcid":false,"given":"Yang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhang","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Ke","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,11]]},"reference":[{"key":"29_CR1","doi-asserted-by":"crossref","unstructured":"Bergmann, P., Meinhardt, T., Leal-Taixe, L.: Tracking without bells and whistles. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 941\u2013951 (2019)","DOI":"10.1109\/ICCV.2019.00103"},{"key":"29_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2008\/246309","volume":"2008","author":"K Bernardin","year":"2008","unstructured":"Bernardin, K., Stiefelhagen, R.: Evaluating multiple object tracking performance: the clear mot metrics. EURASIP J. Image Video Process. 2008, 1\u201310 (2008)","journal-title":"EURASIP J. Image Video Process."},{"key":"29_CR3","doi-asserted-by":"crossref","unstructured":"Bewley, A., Ge, Z., Ott, L., Ramos, F., Upcroft, B.: Simple online and realtime tracking. In: 2016 IEEE International Conference on Image Processing, pp. 3464\u20133468. IEEE (2016)","DOI":"10.1109\/ICIP.2016.7533003"},{"key":"29_CR4","doi-asserted-by":"crossref","unstructured":"Bras\u00f3, G., Leal-Taix\u00e9, L.: Learning a neural solver for multiple object tracking. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6247\u20136257 (2020)","DOI":"10.1109\/CVPR42600.2020.00628"},{"key":"29_CR5","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1016\/j.cviu.2015.06.002","volume":"144","author":"J Chen","year":"2016","unstructured":"Chen, J., Sheng, H., Li, C., Xiong, Z.: PSTG-based multi-label optimization for multi-target tracking. Comput. Vis. Image Underst. 144, 217\u2013227 (2016)","journal-title":"Comput. Vis. Image Underst."},{"key":"29_CR6","doi-asserted-by":"crossref","unstructured":"Chen, X., Qin, Z., An, L., Bhanu, B.: An online learned elementary grouping model for multi-target tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1242\u20131249 (2014)","DOI":"10.1109\/CVPR.2014.162"},{"key":"29_CR7","doi-asserted-by":"crossref","unstructured":"Chu, P., Fan, H., Tan, C.C., Ling, H.: Online multi-object tracking with instance-aware tracker and dynamic model refreshment. In: IEEE Winter Conference on Applications of Computer Vision, pp. 161\u2013170. IEEE (2019)","DOI":"10.1109\/WACV.2019.00023"},{"key":"29_CR8","doi-asserted-by":"crossref","unstructured":"Chu, P., Ling, H.: FAMNet: joint learning of feature, affinity and multi-dimensional assignment for online multiple object tracking. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (2019)","DOI":"10.1109\/ICCV.2019.00627"},{"key":"29_CR9","doi-asserted-by":"crossref","unstructured":"Dai, P., Weng, R., Choi, W., Zhang, C., He, Z., Ding, W.: Learning a proposal classifier for multiple object tracking. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2443\u20132452 (2021)","DOI":"10.1109\/CVPR46437.2021.00247"},{"key":"29_CR10","unstructured":"Dendorfer, P., et al.: MOT20: a benchmark for multi object tracking in crowded scenes. arXiv preprint arXiv:2003.09003 (2020)"},{"key":"29_CR11","doi-asserted-by":"crossref","unstructured":"Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., Tian, Q.: CenterNet: keypoint triplets for object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6569\u20136578 (2019)","DOI":"10.1109\/ICCV.2019.00667"},{"key":"29_CR12","unstructured":"Ge, Z., Liu, S., Wang, F., Li, Z., Sun, J.: YOLOX: exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430 (2021)"},{"key":"29_CR13","doi-asserted-by":"crossref","unstructured":"Ho, K., Kardoost, A., Pfreundt, F.J., Keuper, J., Keuper, M.: A two-stage minimum cost multicut approach to self-supervised multiple person tracking. In: Proceedings of the Asian Conference on Computer Vision (2020)","DOI":"10.1007\/978-3-030-69532-3_33"},{"key":"29_CR14","unstructured":"Hornakova, A., Henschel, R., Rosenhahn, B., Swoboda, P.: Lifted disjoint paths with application in multiple object tracking. In: International Conference on Machine Learning, pp. 4364\u20134375. PMLR (2020)"},{"key":"29_CR15","doi-asserted-by":"crossref","unstructured":"Hornakova, A., Kaiser, T., Swoboda, P., Rolinek, M., Rosenhahn, B., Henschel, R.: Making higher order mot scalable: an efficient approximate solver for lifted disjoint paths. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6330\u20136340 (2021)","DOI":"10.1109\/ICCV48922.2021.00627"},{"issue":"5","key":"29_CR16","doi-asserted-by":"publisher","first-page":"987","DOI":"10.1109\/TPAMI.2011.173","volume":"34","author":"L Kratz","year":"2011","unstructured":"Kratz, L., Nishino, K.: Tracking pedestrians using local spatio-temporal motion patterns in extremely crowded scenes. IEEE Trans. Pattern Anal. Mach. Intell. 34(5), 987\u20131002 (2011)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"29_CR17","unstructured":"Leal-Taix\u00e9, L., Milan, A., Reid, I., Roth, S., Schindler, K.: MOTchallenge 2015: towards a benchmark for multi-target tracking. arXiv preprint arXiv:1504.01942 (2015)"},{"key":"29_CR18","doi-asserted-by":"crossref","unstructured":"Liu, Q., Chu, Q., Liu, B., Yu, N.: GSM: graph similarity model for multi-object tracking. In: IJCAI, pp. 530\u2013536 (2020)","DOI":"10.24963\/ijcai.2020\/74"},{"issue":"2","key":"29_CR19","doi-asserted-by":"publisher","first-page":"548","DOI":"10.1007\/s11263-020-01375-2","volume":"129","author":"J Luiten","year":"2021","unstructured":"Luiten, J., et al.: HOTA: a higher order metric for evaluating multi-object tracking. Int. J. Comput. Vision 129(2), 548\u2013578 (2021)","journal-title":"Int. J. Comput. Vision"},{"key":"29_CR20","unstructured":"Milan, A., Leal-Taix\u00e9, L., Reid, I., Roth, S., Schindler, K.: MOT16: a benchmark for multi-object tracking. arXiv preprint arXiv:1603.00831 (2016)"},{"issue":"10","key":"29_CR21","doi-asserted-by":"publisher","first-page":"2054","DOI":"10.1109\/TPAMI.2015.2505309","volume":"38","author":"A Milan","year":"2015","unstructured":"Milan, A., Schindler, K., Roth, S.: Multi-target tracking by discrete-continuous energy minimization. IEEE Trans. Pattern Anal. Mach. Intell. 38(10), 2054\u20132068 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"29_CR22","doi-asserted-by":"crossref","unstructured":"Mykheievskyi, D., Borysenko, D., Porokhonskyy, V.: Learning local feature descriptors for multiple object tracking. In: Proceedings of the Asian Conference on Computer Vision (2020)","DOI":"10.1007\/978-3-030-69532-3_34"},{"key":"29_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"452","DOI":"10.1007\/978-3-642-15549-9_33","volume-title":"Computer Vision \u2013 ECCV 2010","author":"S Pellegrini","year":"2010","unstructured":"Pellegrini, S., Ess, A., Van Gool, L.: Improving data association by joint modeling of pedestrian trajectories and groupings. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6311, pp. 452\u2013465. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-15549-9_33"},{"key":"29_CR24","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S.: Generalized intersection over union: A metric and a loss for bounding box regression. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 658\u2013666 (2019)","DOI":"10.1109\/CVPR.2019.00075"},{"key":"29_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/978-3-319-48881-3_2","volume-title":"Computer Vision \u2013 ECCV 2016 Workshops","author":"E Ristani","year":"2016","unstructured":"Ristani, E., Solera, F., Zou, R., Cucchiara, R., Tomasi, C.: Performance measures and a data set for\u00a0multi-target, multi-camera tracking. In: Hua, G., J\u00e9gou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 17\u201335. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-48881-3_2"},{"key":"29_CR26","doi-asserted-by":"crossref","unstructured":"Sadeghian, A., Alahi, A., Savarese, S.: Tracking the untrackable: learning to track multiple cues with long-term dependencies. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 300\u2013311 (2017)","DOI":"10.1109\/ICCV.2017.41"},{"issue":"12","key":"29_CR27","doi-asserted-by":"publisher","first-page":"3660","DOI":"10.1109\/TCSVT.2018.2881123","volume":"29","author":"H Sheng","year":"2018","unstructured":"Sheng, H., Chen, J., Zhang, Y., Ke, W., Xiong, Z., Yu, J.: Iterative multiple hypothesis tracking with tracklet-level association. IEEE Trans. Circuits Syst. Video Technol. 29(12), 3660\u20133672 (2018)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"5","key":"29_CR28","doi-asserted-by":"publisher","first-page":"3189","DOI":"10.1109\/JIOT.2020.3015239","volume":"8","author":"H Sheng","year":"2020","unstructured":"Sheng, H., et al.: Combining pose invariant and discriminative features for vehicle reidentification. IEEE Internet Things J. 8(5), 3189\u20133200 (2020)","journal-title":"IEEE Internet Things J."},{"issue":"4","key":"29_CR29","doi-asserted-by":"publisher","first-page":"2193","DOI":"10.1109\/JIOT.2020.3035415","volume":"8","author":"H Sheng","year":"2020","unstructured":"Sheng, H., et al.: Near-online tracking with co-occurrence constraints in blockchain-based edge computing. IEEE Internet Things J. 8(4), 2193\u20132207 (2020)","journal-title":"IEEE Internet Things J."},{"key":"29_CR30","doi-asserted-by":"crossref","unstructured":"Sheng, H., et al.: High confident evaluation for smart city services. Front. Environ. Sci. 10, 1103 (2022)","DOI":"10.3389\/fenvs.2022.950055"},{"key":"29_CR31","doi-asserted-by":"crossref","unstructured":"Stadler, D., Beyerer, J.: Improving multiple pedestrian tracking by track management and occlusion handling. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10958\u201310967 (2021)","DOI":"10.1109\/CVPR46437.2021.01081"},{"key":"29_CR32","doi-asserted-by":"crossref","unstructured":"Stadler, D., Beyerer, J.: Multi-pedestrian tracking with clusters. In: IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 1\u201310. IEEE (2021)","DOI":"10.1109\/AVSS52988.2021.9663829"},{"key":"29_CR33","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1007\/978-3-030-01225-0_30","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Y Sun","year":"2018","unstructured":"Sun, Y., Zheng, L., Yang, Y., Tian, Q., Wang, S.: Beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline). In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11208, pp. 501\u2013518. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01225-0_30"},{"key":"29_CR34","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)"},{"key":"29_CR35","doi-asserted-by":"publisher","first-page":"5257","DOI":"10.1109\/TIP.2022.3192706","volume":"31","author":"S Wang","year":"2022","unstructured":"Wang, S., Sheng, H., Yang, D., Zhang, Y., Wu, Y., Wang, S.: Extendable multiple nodes recurrent tracking framework with RTU++. IEEE Trans. Image Process. 31, 5257\u20135271 (2022)","journal-title":"IEEE Trans. Image Process."},{"key":"29_CR36","doi-asserted-by":"crossref","unstructured":"Wang, S., Sheng, H., Zhang, Y., Wu, Y., Xiong, Z.: A general recurrent tracking framework without real data. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 13219\u201313228 (2021)","DOI":"10.1109\/ICCV48922.2021.01297"},{"issue":"1","key":"29_CR37","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1109\/TCSVT.2020.2975842","volume":"31","author":"J Xiang","year":"2020","unstructured":"Xiang, J., Xu, G., Ma, C., Hou, J.: End-to-end learning deep CRF models for multi-object tracking deep CRF models. IEEE Trans. Circuits Syst. Video Technol. 31(1), 275\u2013288 (2020)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"29_CR38","doi-asserted-by":"crossref","unstructured":"Xu, Y., Chen, Y., Zhang, Y., Zhu, Q., He, Y., Sheng, H.: Bilateral association tracking with Parzen window density estimation. IET Image Processing (2022)","DOI":"10.1049\/ipr2.12633"},{"key":"29_CR39","doi-asserted-by":"crossref","unstructured":"Yang, J., Ge, H., Yang, J., Tong, Y., Su, S.: Online multi-object tracking using multi-function integration and tracking simulation training. Applied Intelligence, pp. 1\u201321 (2021)","DOI":"10.1007\/s10489-021-02457-5"},{"key":"29_CR40","unstructured":"Zhang, L., Li, Y., Nevatia, R.: Global data association for multi-object tracking using network flows. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20138. IEEE (2008)"},{"key":"29_CR41","doi-asserted-by":"publisher","first-page":"6694","DOI":"10.1109\/TIP.2020.2993073","volume":"29","author":"Y Zhang","year":"2020","unstructured":"Zhang, Y., et al.: Long-term tracking with deep tracklet association. IEEE Trans. Image Process. 29, 6694\u20136706 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"29_CR42","doi-asserted-by":"crossref","unstructured":"Zhang, Y., et al.: ByteTrack: multi-object tracking by associating every detection box. arXiv preprint arXiv:2110.06864 (2021)","DOI":"10.1007\/978-3-031-20047-2_1"},{"issue":"11","key":"29_CR43","doi-asserted-by":"publisher","first-page":"3069","DOI":"10.1007\/s11263-021-01513-4","volume":"129","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., Wang, C., Wang, X., Zeng, W., Liu, W.: FairMOT: on the fairness of detection and re-identification in multiple object tracking. Int. J. Comput. Vision 129(11), 3069\u20133087 (2021)","journal-title":"Int. J. Comput. Vision"},{"key":"29_CR44","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1007\/978-3-642-33709-3_23","volume-title":"Computer Vision \u2013 ECCV 2012","author":"X Zhao","year":"2012","unstructured":"Zhao, X., Gong, D., Medioni, G.: Tracking using motion patterns for very crowded scenes. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7573, pp. 315\u2013328. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-33709-3_23"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ACCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-26293-7_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T04:47:48Z","timestamp":1729054068000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-26293-7_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031262920","9783031262937"],"references-count":44,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-26293-7_29","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":"11 March 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Macao","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"4 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2022","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":"accv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.accv2022.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 Microsoft","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"836","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":"277","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":"33% - 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":"3.3","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":"2.6","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":"For the ACCV 2022 workshops 25 papers have been accepted from 40 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)"}}]}}