{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T23:35:54Z","timestamp":1767137754005,"version":"build-2238731810"},"publisher-location":"Cham","reference-count":52,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030682378","type":"print"},{"value":"9783030682385","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-68238-5_8","type":"book-chapter","created":{"date-parts":[[2021,1,30]],"date-time":"2021-01-30T02:02:43Z","timestamp":1611972163000},"page":"104-120","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Real-Time Detection of Multiple Targets from a Moving 360$$^{\\circ }$$ Panoramic Imager in the Wild"],"prefix":"10.1007","author":[{"given":"Boyan","family":"Yuan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nabil","family":"Belbachir","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,1,31]]},"reference":[{"key":"8_CR1","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1007\/978-3-540-28650-9_8","volume-title":"Advanced Lectures on Machine Learning","author":"O Bousquet","year":"2004","unstructured":"Bousquet, O., Boucheron, S., Lugosi, G.: Introduction to statistical learning theory. In: Bousquet, O., von Luxburg, U., R\u00e4tsch, G. (eds.) ML -2003. LNCS (LNAI), vol. 3176, pp. 169\u2013207. Springer, Heidelberg (2004). https:\/\/doi.org\/10.1007\/978-3-540-28650-9_8"},{"key":"8_CR2","doi-asserted-by":"crossref","unstructured":"Thibault, S.: Enhanced surveillance system based on panomorph panoramic lenses. In: Optics and Photonics in Global Homeland Security III, vol. 6540, p. 65400E. International Society for Optics and Photonics (2007)","DOI":"10.1117\/12.719495"},{"key":"8_CR3","doi-asserted-by":"crossref","unstructured":"Thibault, S., Konen, P., Roulet, P., Villegas, M.: Novel hemispheric image formation: concepts and applications. In: Photon Management III, vol. 6994, p. 699406. International Society for Optics and Photonics (2008)","DOI":"10.1117\/12.781597"},{"issue":"10","key":"8_CR4","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2009","unstructured":"Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345\u20131359 (2009)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"8_CR5","doi-asserted-by":"crossref","unstructured":"Li, C., Parikh, D., Chen, T.: Automatic discovery of groups of objects for scene understanding. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2735\u20132742. IEEE (2012)","DOI":"10.1109\/CVPR.2012.6247996"},{"key":"8_CR6","doi-asserted-by":"crossref","unstructured":"Moo Yi, K., Yun, K., Wan Kim, S., Jin Chang, H., Young Choi, J.: Detection of moving objects with non-stationary cameras in 5.8 ms: bringing motion detection to your mobile device. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 27\u201334 (2013)","DOI":"10.1109\/CVPRW.2013.9"},{"issue":"3","key":"8_CR7","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1109\/TPAMI.2014.2345390","volume":"37","author":"JF Henriques","year":"2014","unstructured":"Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583\u2013596 (2014)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"8_CR8","doi-asserted-by":"crossref","unstructured":"Caruso, D., Engel, J., Cremers, D.: Large-scale direct slam for omnidirectional cameras. In: 2015 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 141\u2013148. IEEE (2015)","DOI":"10.1109\/IROS.2015.7353366"},{"issue":"2","key":"8_CR9","doi-asserted-by":"publisher","first-page":"696","DOI":"10.1109\/TIP.2016.2627816","volume":"26","author":"A Furnari","year":"2016","unstructured":"Furnari, A., Farinella, G.M., Bruna, A.R., Battiato, S.: Affine covariant features for fisheye distortion local modeling. IEEE Trans. Image Process. 26(2), 696\u2013710 (2016)","journal-title":"IEEE Trans. Image Process."},{"key":"8_CR10","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"8_CR11","doi-asserted-by":"crossref","unstructured":"Nguyen, T.B., Chung, S.T., et al.: ConvNets and AGMM based real-time human detection under fisheye camera for embedded surveillance. In: 2016 International Conference on Information and Communication Technology Convergence (ICTC), pp. 840\u2013845. IEEE (2016)","DOI":"10.1109\/ICTC.2016.7763311"},{"issue":"5","key":"8_CR12","doi-asserted-by":"publisher","first-page":"879","DOI":"10.1109\/TPAMI.2016.2564408","volume":"39","author":"A Rozantsev","year":"2016","unstructured":"Rozantsev, A., Lepetit, V., Fua, P.: Detecting flying objects using a single moving camera. IEEE Trans. Pattern Anal. Mach. Intell. 39(5), 879\u2013892 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"8_CR13","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"8_CR14","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Rebecq, H., Forster, C., Scaramuzza, D.: Benefit of large field-of-view cameras for visual odometry. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 801\u2013808. IEEE (2016)","DOI":"10.1109\/ICRA.2016.7487210"},{"key":"8_CR15","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961\u20132969 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"8_CR16","doi-asserted-by":"crossref","unstructured":"Hu, H.-N., Lin, Y.C., Liu, M.Y., Cheng, H.T., Chang, Y.J., Sun, M.: Deep 360 pilot: learning a deep agent for piloting through 360deg sports videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3451\u20133460 (2017)","DOI":"10.1109\/CVPR.2017.153"},{"key":"8_CR17","unstructured":"Kawaguchi, K., Kaelbling, L.P., Bengio, Y.: Generalization in deep learning. arXiv preprint arXiv:1710.05468 (2017)"},{"key":"8_CR18","unstructured":"Neyshabur, B., Bhojanapalli, S., McAllester, D., Srebro, N.: Exploring generalization in deep learning. In: Advances in Neural Information Processing Systems, pp. 5947\u20135956 (2017)"},{"issue":"6","key":"8_CR19","doi-asserted-by":"publisher","first-page":"1341","DOI":"10.3390\/s17061341","volume":"17","author":"L Ran","year":"2017","unstructured":"Ran, L., Zhang, Y., Zhang, Q., Yang, T.: Convolutional neural network-based robot navigation using uncalibrated spherical images. Sensors 17(6), 1341 (2017)","journal-title":"Sensors"},{"key":"8_CR20","unstructured":"Su, Y.-C., Grauman, K.: Learning spherical convolution for fast features from 360 imagery. In: Advances in Neural Information Processing Systems, pp. 529\u2013539 (2017)"},{"key":"8_CR21","doi-asserted-by":"crossref","unstructured":"Baek, I., Davies, A., Yan, G., Rajkumar, R.R.: Real-time detection, tracking, and classification of moving and stationary objects using multiple fisheye images. In: 2018 IEEE Intelligent Vehicles Symposium (IV), pp. 447\u2013452. IEEE (2018)","DOI":"10.1109\/IVS.2018.8500455"},{"key":"8_CR22","unstructured":"Chung, Y., Haas, P.J., Upfal, E., Kraska, T.: Unknown examples & machine learning model generalization. arXiv preprint arXiv:1808.08294 (2018)"},{"key":"8_CR23","unstructured":"Cohen, T.S., Geiger, M., K\u00f6hler, J., Welling, M.: Spherical CNNs. arXiv preprint arXiv:1801.10130 (2018)"},{"key":"8_CR24","doi-asserted-by":"crossref","unstructured":"Coors, B., Paul Condurache, A., Geiger, A.: SphereNet: learning spherical representations for detection and classification in omnidirectional images. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 518\u2013533 (2018)","DOI":"10.1007\/978-3-030-01240-3_32"},{"key":"8_CR25","doi-asserted-by":"crossref","unstructured":"Esteves, C., Allen-Blanchette, C., Makadia, A., Daniilidis, K.: Learning so (3) equivariant representations with spherical CNNs. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 52\u201368 (2018)","DOI":"10.1007\/978-3-030-01261-8_4"},{"key":"8_CR26","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.neucom.2017.08.071","volume":"280","author":"SV Georgakopoulos","year":"2018","unstructured":"Georgakopoulos, S.V., Kottari, K., Delibasis, K., Plagianakos, V.P., Maglogiannis, I.: Pose recognition using convolutional neural networks on omni-directional images. Neurocomputing 280, 23\u201331 (2018)","journal-title":"Neurocomputing"},{"key":"8_CR27","unstructured":"Kolter, Z., Madry, A.: Adversarial robustness: theory and practice. Tutorial at NeurIPS (2018)"},{"key":"8_CR28","unstructured":"Novak, R., Bahri, Y., Abolafia, D.A., Pennington, J., Sohl-Dickstein, J.: Sensitivity and generalization in neural networks: an empirical study. arXiv preprint arXiv:1802.08760 (2018)"},{"key":"8_CR29","unstructured":"Roulet, P., et al.: Method to capture, store, distribute, share, stream and display panoramic image or video, US Patent App. 15\/656,707, 24 May 2018"},{"key":"8_CR30","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510\u20134520 (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"8_CR31","doi-asserted-by":"crossref","unstructured":"Sheng, T., Feng, C., Zhuo, S., Zhang, X., Shen, L., Aleksic, M.: A quantization-friendly separable convolution for MobileNets. In: 2018 1st Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications (EMC2), pp. 14\u201318. IEEE (2018)","DOI":"10.1109\/EMC2.2018.00011"},{"key":"8_CR32","doi-asserted-by":"crossref","unstructured":"Xu, Y., et al.: Gaze prediction in dynamic 360 immersive videos. In: proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5333\u20135342 (2018)","DOI":"10.1109\/CVPR.2018.00559"},{"key":"8_CR33","unstructured":"Defferrard, M., Milani, M., Gusset, F., Perraudin, N.: DeepSphere: a graph-based spherical CNN. In: International Conference on Learning Representations (2019)"},{"key":"8_CR34","doi-asserted-by":"crossref","unstructured":"Hossain, S., Lee, D.j.: Deep learning-based real-time multiple-object detection and tracking from aerial imagery via a flying robot with GPU-based embedded devices. Sensors 19(15), 3371 (2019)","DOI":"10.3390\/s19153371"},{"issue":"3","key":"8_CR35","first-page":"172988141984889","volume":"16","author":"YC Hou","year":"2019","unstructured":"Hou, Y.C., Sahari, K.S.M., How, D.N.T.: A review on modeling of flexible deformable object for dexterous robotic manipulation. Int. J. Adv. Rob. Syst. 16(3), 1729881419848894 (2019)","journal-title":"Int. J. Adv. Rob. Syst."},{"key":"8_CR36","doi-asserted-by":"crossref","unstructured":"Howard, A., et al.: Searching for MobileNetV3. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1314\u20131324 (2019)","DOI":"10.1109\/ICCV.2019.00140"},{"key":"8_CR37","doi-asserted-by":"crossref","unstructured":"Ignatov, A., et al.: AI benchmark: all about deep learning on smartphones in 2019. In: 2019 IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 3617\u20133635. IEEE (2019)","DOI":"10.1109\/ICCVW.2019.00447"},{"key":"8_CR38","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.ascom.2019.03.004","volume":"27","author":"N Perraudin","year":"2019","unstructured":"Perraudin, N., Defferrard, M., Kacprzak, T., Sgier, R.: DeepSphere: efficient spherical convolutional neural network with HEALPix sampling for cosmological applications. Astron. Comput. 27, 130\u2013146 (2019)","journal-title":"Astron. Comput."},{"key":"8_CR39","doi-asserted-by":"crossref","unstructured":"Reuther, A., Michaleas, P., Jones, M., Gadepally, V., Samsi, S., Kepner, J.: Survey and benchmarking of machine learning accelerators. arXiv preprint arXiv:1908.11348 (2019)","DOI":"10.1109\/HPEC.2019.8916327"},{"key":"8_CR40","doi-asserted-by":"crossref","unstructured":"Wang, K.-H., Lai, S.-H.: Object detection in curved space for 360-degree camera. In: ICASSP 2019\u20132019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3642\u20133646. IEEE (2019)","DOI":"10.1109\/ICASSP.2019.8683093"},{"key":"8_CR41","doi-asserted-by":"crossref","unstructured":"Wang, T., Hsieh, Y.Y., Wong, F.W., Chen, Y.F.: Mask-RCNN based people detection using a top-view fisheye camera. In: 2019 International Conference on Technologies and Applications of Artificial Intelligence (TAAI), pp. 1\u20134. IEEE (2019)","DOI":"10.1109\/TAAI48200.2019.8959887"},{"issue":"11","key":"8_CR42","doi-asserted-by":"publisher","first-page":"2622","DOI":"10.3390\/s19112622","volume":"19","author":"D Yu","year":"2019","unstructured":"Yu, D., Ji, S.: Grid based spherical CNN for object detection from panoramic images. Sensors 19(11), 2622 (2019)","journal-title":"Sensors"},{"key":"8_CR43","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.jvcir.2019.01.005","volume":"59","author":"J Zhu","year":"2019","unstructured":"Zhu, J., Zhu, J., Wan, X., Wu, C., Xu, C.: Object detection and localization in 3D environment by fusing raw fisheye image and attitude data. J. Vis. Commun. Image Represent. 59, 128\u2013139 (2019)","journal-title":"J. Vis. Commun. Image Represent."},{"key":"8_CR44","doi-asserted-by":"crossref","unstructured":"Chen, Y., Dai, X., Liu, M., Chen, D., Yuan, L., Liu, Z.: Dynamic convolution: attention over convolution kernels. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11030\u201311039 (2020)","DOI":"10.1109\/CVPR42600.2020.01104"},{"key":"8_CR45","doi-asserted-by":"crossref","unstructured":"Chou, S.-H., Sun, C., Chang, W.Y., Hsu, W.T., Sun, M., Fu, J.: 360-indoor: towards learning real-world objects in 360deg indoor equirectangular images. In: The IEEE Winter Conference on Applications of Computer Vision, pp. 845\u2013853 (2020)","DOI":"10.1109\/WACV45572.2020.9093262"},{"key":"8_CR46","doi-asserted-by":"publisher","first-page":"115751","DOI":"10.1016\/j.image.2019.115751","volume":"82","author":"F Farahi","year":"2020","unstructured":"Farahi, F., Yazdi, H.S.: Probabilistic Kalman filter for moving object tracking. Sig. Process. Image Commun. 82, 115751 (2020)","journal-title":"Sig. Process. Image Commun."},{"key":"8_CR47","doi-asserted-by":"crossref","unstructured":"Gkitsas, V., Zioulis, N., Alvarez, F., Zarpalas, D., Daras, P.: Deep lighting environment map estimation from spherical panoramas. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 640\u2013641 (2020)","DOI":"10.1109\/CVPRW50498.2020.00328"},{"key":"8_CR48","doi-asserted-by":"crossref","unstructured":"Jin, L., et al.: Geometric structure based and regularized depth estimation from 360 indoor imagery. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 889\u2013898 (2020)","DOI":"10.1109\/CVPR42600.2020.00097"},{"key":"8_CR49","doi-asserted-by":"publisher","unstructured":"Lee, Y., Jeong, J., Yun, J., Cho, W., Yoon, K.-J.: SpherePHD: applying CNNs on 360$$^{\\circ }$$ images with non-Euclidean spherical PolyHeDron representation. IEEE Trans. Pattern Anal. Mach. Intell., 1 (2020). https:\/\/doi.org\/10.1109\/TPAMI.2020.2997045","DOI":"10.1109\/TPAMI.2020.2997045"},{"key":"8_CR50","doi-asserted-by":"crossref","unstructured":"Yan, X., Acuna, D., Fidler, S.: Neural data server: a large-scale search engine for transfer learning data. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3893\u20133902 (2020)","DOI":"10.1109\/CVPR42600.2020.00395"},{"key":"8_CR51","doi-asserted-by":"crossref","unstructured":"Yang, Q., Li, C., Dai, W., Zou, J., Qi, G.J., Xiong, H.: Rotation equivariant graph convolutional network for spherical image classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4303\u20134312 (2020)","DOI":"10.1109\/CVPR42600.2020.00436"},{"key":"8_CR52","doi-asserted-by":"crossref","unstructured":"Zhao, P., You, A., Zhang, Y., Liu, J., Bian, K., Tong, Y.: Spherical criteria for fast and accurate 360 object detection. In: AAAI, pp. 12959\u201312966 (2020)","DOI":"10.1609\/aaai.v34i07.6995"}],"updated-by":[{"DOI":"10.1007\/978-3-030-68238-5_49","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T00:00:00Z","timestamp":1620691200000}}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-68238-5_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T18:06:07Z","timestamp":1738173967000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-68238-5_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030682378","9783030682385"],"references-count":52,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-68238-5_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"31 January 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"11 May 2021","order":2,"name":"change_date","label":"Change Date","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"Correction","order":3,"name":"change_type","label":"Change Type","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"In the originally published version of chapter 8 an acknowledgement was missing. This has been corrected.","order":4,"name":"change_details","label":"Change Details","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":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","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":"eccv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2020.eu\/","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":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5025","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":"1360","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":"27% - 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":"7","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":"The conference was held virtually due to the COVID-19 pandemic. From the ECCV Workshops 249 full papers, 18 short papers, and 21 further contributions were published out of a total of 467 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)"}}]}}