{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T16:45:04Z","timestamp":1742921104843,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031250552"},{"type":"electronic","value":"9783031250569"}],"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\/978-3-031-25056-9_44","type":"book-chapter","created":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T12:09:56Z","timestamp":1676376596000},"page":"697-708","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Ego-Motion Compensation of\u00a0Range-Beam-Doppler Radar Data for\u00a0Object Detection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5824-329X","authenticated-orcid":false,"given":"Michael","family":"Meyer","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6700-8395","authenticated-orcid":false,"given":"Marc","family":"Unzueta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6217-2241","authenticated-orcid":false,"given":"Georg","family":"Kuschk","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5825-8915","authenticated-orcid":false,"given":"Sven","family":"Tomforde","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,15]]},"reference":[{"key":"44_CR1","doi-asserted-by":"crossref","unstructured":"Scheck, T., Mallandur, A., Wiede, C., Hirtz, G.: Where to drive: free space detection with one fisheye camera. In: Twelfth International Conference on Machine Vision (ICMV 2019) (2020)","DOI":"10.1117\/12.2556380"},{"key":"44_CR2","doi-asserted-by":"crossref","unstructured":"Lang, A.H., Vora, S., Caesar, H., Zhou, L., Yang, J., Beijbom, O.: PointPillars: fast encoders for object detection from point clouds. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12697\u201312705 (2019)","DOI":"10.1109\/CVPR.2019.01298"},{"key":"44_CR3","doi-asserted-by":"crossref","unstructured":"Meyer, G.P., Laddha, A., Kee, E., Vallespi-Gonzalez, C., Wellington, C.K.: LaserNet: an efficient probabilistic 3D object detector for autonomous driving. In: CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12669\u201312678 (2019). IEEE (2019)","DOI":"10.1109\/CVPR.2019.01296"},{"key":"44_CR4","doi-asserted-by":"crossref","unstructured":"Meyer, M., Kuschk, G., Tomforde, S.: Complex-valued convolutional neural networks for automotive scene classification based on range-beam-doppler tensors. In: 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) (2020)","DOI":"10.1109\/ITSC45102.2020.9294335"},{"key":"44_CR5","unstructured":"Lim, T.Y., et al.: Radar and camera early fusion for vehicle detection in advanced driver assistance systems. In: Conference on Neural Information Processing Systems Workshops (2019)"},{"key":"44_CR6","doi-asserted-by":"crossref","unstructured":"Orr, I., Cohen, M., Zalevsky, Z.: High-resolution radar road segmentation using weakly supervised learning. In: Nature Machine Intelligence, pp. 1\u20138 (2021)","DOI":"10.1038\/s42256-020-00288-6"},{"key":"44_CR7","doi-asserted-by":"crossref","unstructured":"Scheiner, N., et al.: Seeing around street corners: non-line-of-sight detection and tracking in-the-wild using doppler radar. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2068\u20132077 (2020)","DOI":"10.1109\/CVPR42600.2020.00214"},{"key":"44_CR8","doi-asserted-by":"crossref","unstructured":"Dong, X., Wang, P., Zhang, P., Liu, L.: Probabilistic oriented object detection in automotive radar. In: Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 458\u2013467 (2020)","DOI":"10.1109\/CVPRW50498.2020.00059"},{"key":"44_CR9","doi-asserted-by":"crossref","unstructured":"Kothari, R., Kariminezhad, A., Mayr, C., Zhang, H.: Object detection and heading forecasting by fusing raw radar data using cross attention. CoRR (2022). https:\/\/doi.org\/10.48550\/arXiv.2205.08406","DOI":"10.1109\/IV55152.2023.10186591"},{"key":"44_CR10","doi-asserted-by":"crossref","unstructured":"Zhang, A., Nowruzi, F.E., Laganiere, R.: Raddet: range-azimuth-doppler based radar object detection for dynamic road users. In: 2021 18th Conference on Robots and Vision (CRV), pp. 95\u2013102 (2021)","DOI":"10.1109\/CRV52889.2021.00021"},{"key":"44_CR11","doi-asserted-by":"crossref","unstructured":"Major, B., et al.: Vehicle detection with automotive radar using deep learning on range-azimuth-doppler tensors. In: International Conference on Computer Vision Workshops (2019)","DOI":"10.1109\/ICCVW.2019.00121"},{"key":"44_CR12","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"44_CR13","unstructured":"Jaderberg, M., Simonyan, K., Zisserman, A., kavukcuoglu, k.: Spatial transformer networks. In: Cortes, C., Lawrence, N., Lee, D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 28. Curran Associates, Inc. (2015)"},{"issue":"2","key":"44_CR14","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1109\/LRA.2020.2967272","volume":"5","author":"A Palffy","year":"2020","unstructured":"Palffy, A., Dong, J., Kooij, J.F., Gavrila, D.M.: CNN based road user detection using the 3D radar cube. IEEE Robot. Autom. Lett. 5(2), 1263\u20131270 (2020)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"44_CR15","doi-asserted-by":"crossref","unstructured":"Niederl\u00f6hner, D., et al.: Self-supervised velocity estimation for automotive radar object detection networks (2022)","DOI":"10.1109\/IV51971.2022.9827295"},{"key":"44_CR16","doi-asserted-by":"crossref","unstructured":"Ulrich, M., et al.: Improved orientation estimation and detection with hybrid object detection networks for automotive radar (2022)","DOI":"10.1109\/ITSC55140.2022.9922457"},{"key":"44_CR17","unstructured":"Iovescu, C., Rao, S.: The fundamentals of millimeter wave sensors. In: Texas Instruments, pp. 1\u20138 (2017)"},{"issue":"4","key":"44_CR18","doi-asserted-by":"publisher","first-page":"1362","DOI":"10.3390\/s21041362","volume":"21","author":"Y Albagory","year":"2021","unstructured":"Albagory, Y.: An efficient conformal stacked antenna array design and 3D-beamforming for UAV and space vehicle communications. Sensors 21(4), 1362 (2021)","journal-title":"Sensors"},{"key":"44_CR19","unstructured":"Skolnik, M.I.: Radar handbook. McGraw-Hill Education (2008)"},{"key":"44_CR20","doi-asserted-by":"crossref","unstructured":"Stoica, P., Li, J., Xie, Y.: On probing signal design for MIMO radar. IEEE Trans. Sig. Process. 55(8), 4151\u20134161 (2007)","DOI":"10.1109\/TSP.2007.894398"},{"key":"44_CR21","doi-asserted-by":"crossref","unstructured":"Meyer, M., Kuschk, G., Tomforde, S.: Graph convolutional networks for 3D object detection on radar data. In: IEEE\/CVF International Conference on Computer Vision (ICCV) Workshops (2021)","DOI":"10.1109\/ICCVW54120.2021.00340"},{"key":"44_CR22","unstructured":"Roddick, T., Kendall, A., Cipolla, R.: Orthographic feature transform for monocular 3D object detection. CoRR (2018). http:\/\/arxiv.org\/abs\/1811.08188"},{"key":"44_CR23","doi-asserted-by":"crossref","unstructured":"Meyer, M., Nekkah, S., Kuschk, G., Tomforde, S.: Automotive object detection on highly compressed range-beam-doppler radar data. In: EuRAD (2022)","DOI":"10.23919\/EuRAD54643.2022.9924839"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-25056-9_44","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T18:37:33Z","timestamp":1710268653000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-25056-9_44"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031250552","9783031250569"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-25056-9_44","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":"15 February 2023","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":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","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":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","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":"5804","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":"1645","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":"28% - 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":"3.21","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":"3.91","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":"From the workshops, 367 reviewed full papers have been selected for publication","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)"}}]}}