{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T13:31:46Z","timestamp":1742995906741,"version":"3.40.3"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031730382"},{"type":"electronic","value":"9783031730399"}],"license":[{"start":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T00:00:00Z","timestamp":1730332800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T00:00:00Z","timestamp":1730332800000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-73039-9_17","type":"book-chapter","created":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T14:57:07Z","timestamp":1730300227000},"page":"294-310","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CARB-Net: Camera-Assisted Radar-Based Network for\u00a0Vulnerable Road User Detection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5552-6446","authenticated-orcid":false,"given":"Wei-Yu","family":"Lee","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4477-7746","authenticated-orcid":false,"given":"Martin","family":"Dimitrievski","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2112-3475","authenticated-orcid":false,"given":"David","family":"Van Hamme","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5543-2631","authenticated-orcid":false,"given":"Jan","family":"Aelterman","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8790-1116","authenticated-orcid":false,"given":"Ljubomir","family":"Jovanov","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4456-4353","authenticated-orcid":false,"given":"Wilfried","family":"Philips","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,31]]},"reference":[{"key":"17_CR1","doi-asserted-by":"crossref","unstructured":"Barnes, D., Gadd, M., Murcutt, P., Newman, P., Posner, I.: The Oxford radar robotcar dataset: a radar extension to the oxford robotcar dataset. In: ICRA (2020)","DOI":"10.1109\/ICRA40945.2020.9196884"},{"key":"17_CR2","doi-asserted-by":"crossref","unstructured":"Caesar, H., et al.: nuScenes: a multimodal dataset for autonomous driving. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"17_CR3","doi-asserted-by":"crossref","unstructured":"Chen, S., He, W., Ren, J., Jiang, X.: Attention-based dual-stream vision transformer for radar gait recognition. In: ICASSP. IEEE (2022)","DOI":"10.1109\/ICASSP43922.2022.9746565"},{"key":"17_CR4","doi-asserted-by":"crossref","unstructured":"Chen, X., Ma, H., Wan, J., Li, B., Xia, T.: Multi-view 3d object detection network for autonomous driving. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.691"},{"key":"17_CR5","doi-asserted-by":"crossref","unstructured":"Dimitrievski, M., Shopovska, I., Van\u00a0Hamme, D., Veelaert, P., Philips, W.: Weakly supervised deep learning method for vulnerable road user detection in FMCW radar. In: IEEE International Conference on Intelligent Transportation Systems (ITSC). IEEE (2020)","DOI":"10.1109\/ITSC45102.2020.9294399"},{"key":"17_CR6","doi-asserted-by":"crossref","unstructured":"Gao, X., Xing, G., Roy, S., Liu, H.: Experiments with mmWave automotive radar test-bed. In: 2019 53rd Asilomar Conference on Signals, Systems, and Computers, pp.\u00a01\u20136. IEEE (2019)","DOI":"10.1109\/IEEECONF44664.2019.9048939"},{"key":"17_CR7","doi-asserted-by":"publisher","first-page":"5119","DOI":"10.1109\/JSEN.2020.3036047","volume":"21","author":"X Gao","year":"2020","unstructured":"Gao, X., Xing, G., Roy, S., Liu, H.: RAMP-CNN: a novel neural network for enhanced automotive radar object recognition. IEEE Sens. J. 21, 5119\u20135132 (2020)","journal-title":"IEEE Sens. J."},{"key":"17_CR8","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1109\/JPROC.2014.2365517","volume":"103","author":"H Griffiths","year":"2014","unstructured":"Griffiths, H., et al.: Radar spectrum engineering and management: technical and regulatory issues. Proc. IEEE 103, 85\u2013102 (2014)","journal-title":"Proc. IEEE"},{"key":"17_CR9","doi-asserted-by":"crossref","unstructured":"Gusland, D., Christiansen, J.M., Torvik, B., Fioranelli, F., Gurbuz, S.Z., Ritchie, M.: Open radar initiative: large scale dataset for benchmarking of micro-doppler recognition algorithms. In: IEEE Radar Conference (RadarConf), pp.\u00a01\u20136 (2021)","DOI":"10.1109\/RadarConf2147009.2021.9455239"},{"key":"17_CR10","volume-title":"Multiple View Geometry in Computer Vision","author":"R Hartley","year":"2003","unstructured":"Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2003)"},{"key":"17_CR11","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1007\/978-3-031-19839-7_23","volume-title":"ECCV 2022","author":"JJ Hwang","year":"2022","unstructured":"Hwang, J.J., et al.: CramNet: camera-radar fusion with ray-constrained cross-attention for robust 3D object detection. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13698, pp. 388\u2013405. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19839-7_23"},{"key":"17_CR12","unstructured":"Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics (2023). https:\/\/github.com\/ultralytics\/ultralytics"},{"key":"17_CR13","doi-asserted-by":"crossref","unstructured":"Kim, Y., Kim, S., Choi, J.W., Kum, D.: Craft: camera-radar 3D object detection with spatio-contextual fusion transformer. In: AAAI (2023)","DOI":"10.1609\/aaai.v37i1.25198"},{"key":"17_CR14","doi-asserted-by":"crossref","unstructured":"Kim, Y., Shin, J., Kim, S., Lee, I.J., Choi, J.W., Kum, D.: CRN: camera radar net for accurate, robust, efficient 3D perception. In: ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.01615"},{"key":"17_CR15","doi-asserted-by":"crossref","unstructured":"Kopp, J., Kellner, D., Piroli, A., Dietmayer, K.: Tackling clutter in radar data\u2013label generation and detection using pointNet++. arXiv preprint arXiv:2303.09530 (2023)","DOI":"10.1109\/ICRA48891.2023.10160222"},{"key":"17_CR16","doi-asserted-by":"crossref","unstructured":"Lee, W.Y., Dimitrievski, M., Jovanov, L., Philips, W.: Spatio-temporal consistency for semi-supervised learning using 3D radar cubes. In: IEEE Intelligent Vehicles Symposium (IV). IEEE (2021)","DOI":"10.1109\/IV48863.2021.9575247"},{"key":"17_CR17","doi-asserted-by":"crossref","unstructured":"Lee, W.Y., Jovanov, L., Kumcu, A., Philips, W.: ARC: automotive radar consistency regularization for semi-supervised learning. IEEE Trans. Intell. Veh., 1\u201316 (2023)","DOI":"10.1109\/TIV.2023.3318368"},{"key":"17_CR18","doi-asserted-by":"crossref","unstructured":"Lee, W.Y., Jovanov, L., Philips, W.: Multi-view target transformation for pedestrian detection. In: WACV Workshops (2023)","DOI":"10.1109\/WACVW58289.2023.00014"},{"key":"17_CR19","doi-asserted-by":"crossref","unstructured":"Li, Y., et al.: BEVDepth: acquisition of reliable depth for multi-view 3D object detection. In: AAAI (2023)","DOI":"10.1609\/aaai.v37i2.25233"},{"key":"17_CR20","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-031-20077-9_1","volume-title":"ECCV 2022","author":"Z Li","year":"2022","unstructured":"Li, Z., et al.: BEVFormer: learning bird\u2019s-eye-view representation from multi-camera images via spatiotemporal transformers. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13669, pp. 1\u201318. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-20077-9_1"},{"key":"17_CR21","unstructured":"Lim, T.Y., Ansari, A., Major, B., Fontijne, D., Hamilton, M., Gowaikar, R., Subramanian, S.: Radar and camera early fusion for vehicle detection in advanced driver assistance systems. In: NIPS Workshops (2019)"},{"key":"17_CR22","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"17_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/978-3-319-46448-0_2","volume-title":"Computer Vision \u2013 ECCV 2016","author":"W Liu","year":"2016","unstructured":"Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21\u201337. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2"},{"key":"17_CR24","unstructured":"Liu, Y., Wang, F., Wang, N., ZHANG, Z.X.: Echoes beyond points: unleashing the power of raw radar data in multi-modality fusion. In: NeurIPS (2024)"},{"key":"17_CR25","unstructured":"Liwen, Z., et al.: PeakConv: learning peak receptive field for radar semantic segmentation. In: CVPR (2023)"},{"key":"17_CR26","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"17_CR27","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1007\/BF00201978","volume":"64","author":"HA Mallot","year":"1991","unstructured":"Mallot, H.A., B\u00fclthoff, H.H., Little, J.J., Bohrer, S.: Inverse perspective mapping simplifies optical flow computation and obstacle detection. Biol. Cybern. 64, 177\u2013185 (1991)","journal-title":"Biol. Cybern."},{"key":"17_CR28","unstructured":"Meyer, M., Kuschk, G.: Automotive radar dataset for deep learning based 3D object detection. In: European Radar Conference (EuRAD) (2019)"},{"key":"17_CR29","doi-asserted-by":"crossref","unstructured":"Ouaknine, A., Newson, A., P\u00e9rez, P., Tupin, F., Rebut, J.: Multi-view radar semantic segmentation. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.01538"},{"key":"17_CR30","doi-asserted-by":"crossref","unstructured":"Ouaknine, A., Newson, A., Rebut, J., Tupin, F., P\u00e9rez, P.: CARRADA dataset: camera and automotive radar with range- angle- doppler annotations. In: ICPR (2021)","DOI":"10.1109\/ICPR48806.2021.9413181"},{"key":"17_CR31","unstructured":"Paek, D.H., Kong, S.H., Wijaya, K.T.: K-radar: 4D radar object detection for autonomous driving in various weather conditions. In: NeurIPS (2022)"},{"key":"17_CR32","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1007\/978-3-030-58568-6_12","volume-title":"Computer Vision \u2013 ECCV 2020","author":"J Philion","year":"2020","unstructured":"Philion, J., Fidler, S.: Lift, splat, shoot: encoding images from arbitrary camera rigs by implicitly unprojecting to 3D. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12359, pp. 194\u2013210. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58568-6_12"},{"key":"17_CR33","unstructured":"Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNet++: deep hierarchical feature learning on point sets in a metric space. In: NIPS (2017)"},{"key":"17_CR34","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"17_CR35","doi-asserted-by":"crossref","unstructured":"Schumann, O., Hahn, M., Dickmann, J., W\u00f6hler, C.: Semantic segmentation on radar point clouds. In: International Conference on Information Fusion (FUSION) (2018)","DOI":"10.23919\/ICIF.2018.8455344"},{"key":"17_CR36","doi-asserted-by":"crossref","unstructured":"Sheeny, M., De\u00a0Pellegrin, E., Mukherjee, S., Ahrabian, A., Wang, S., Wallace, A.: Radiate: a radar dataset for automotive perception in bad weather. In: ICRA (2021)","DOI":"10.1109\/ICRA48506.2021.9562089"},{"key":"17_CR37","doi-asserted-by":"crossref","unstructured":"Shi, W., et al.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.207"},{"key":"17_CR38","doi-asserted-by":"crossref","unstructured":"Vaizman, Y., Weibel, N., Lanckriet, G.: Context recognition in-the-wild: unified model for multi-modal sensors and multi-label classification. In: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (2018)","DOI":"10.1145\/3161192"},{"key":"17_CR39","doi-asserted-by":"crossref","unstructured":"Wang, W., Tran, D., Feiszli, M.: What makes training multi-modal classification networks hard? In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01271"},{"key":"17_CR40","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wang, G., Hsu, H.M., Liu, H., Hwang, J.N.: Rethinking of radar\u2019s role: a camera-radar dataset and systematic annotator via coordinate alignment. In: CVPR (2021)","DOI":"10.1109\/CVPRW53098.2021.00316"},{"key":"17_CR41","doi-asserted-by":"publisher","first-page":"2094","DOI":"10.1109\/TIV.2023.3307157","volume":"9","author":"S Yao","year":"2023","unstructured":"Yao, S., et al.: Radar-camera fusion for object detection and semantic segmentation in autonomous driving: a comprehensive review. IEEE Trans. Intell. Veh. 9, 2094\u20132128 (2023)","journal-title":"IEEE Trans. Intell. Veh."},{"key":"17_CR42","doi-asserted-by":"publisher","first-page":"1523","DOI":"10.1109\/TIV.2023.3240287","volume":"8","author":"T Zhou","year":"2023","unstructured":"Zhou, T., Chen, J., Shi, Y., Jiang, K., Yang, M., Yang, D.: Bridging the view disparity between radar and camera features for multi-modal fusion 3d object detection. IEEE Trans. Intell. Veh. 8, 1523\u20131535 (2023)","journal-title":"IEEE Trans. Intell. Veh."}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73039-9_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T15:25:18Z","timestamp":1730301918000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73039-9_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,31]]},"ISBN":["9783031730382","9783031730399"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73039-9_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,10,31]]},"assertion":[{"value":"31 October 2024","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":"Milan","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":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}