{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T17:05:39Z","timestamp":1793207139012,"version":"4.5.0"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030880033","type":"print"},{"value":"9783030880040","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-88004-0_2","type":"book-chapter","created":{"date-parts":[[2021,10,21]],"date-time":"2021-10-21T19:06:25Z","timestamp":1634843185000},"page":"16-28","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["3D Multi-object Detection and Tracking with Sparse Stationary LiDAR"],"prefix":"10.1007","author":[{"given":"Meng","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiyu","family":"Pan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianjiang","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,10,22]]},"reference":[{"key":"2_CR1","doi-asserted-by":"crossref","unstructured":"Buric, M., Ivasic-Kos, M., Pobar, M.: Player tracking in sports videos. In: CloudCom (2019)","DOI":"10.1109\/CloudCom.2019.00058"},{"key":"2_CR2","doi-asserted-by":"crossref","unstructured":"Caesar, H., Bankiti, V., Lang, A.H.: nuScenes: a multimodal dataset for autonomous driving. CoRR (2019)","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"2_CR3","doi-asserted-by":"crossref","unstructured":"Chiu, H., Prioletti, A., Li, J., Bohg, J.: Probabilistic 3D multi-object tracking for autonomous driving. CoRR (2020)","DOI":"10.1109\/ICRA48506.2021.9561754"},{"key":"2_CR4","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the KITTI vision benchmark suite. In: CVPR (2012)","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"2_CR5","doi-asserted-by":"crossref","unstructured":"Kurach, K., et al.: Google research football: a novel reinforcement learning environment. In: AAAI (2020)","DOI":"10.1609\/aaai.v34i04.5878"},{"key":"2_CR6","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: CVPR (2019)","DOI":"10.1109\/CVPR.2019.01298"},{"key":"2_CR7","doi-asserted-by":"crossref","unstructured":"Li, B.: 3D fully convolutional network for vehicle detection in point cloud. In: IROS (2017)","DOI":"10.1109\/IROS.2017.8205955"},{"key":"2_CR8","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, 318\u2013327 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2_CR9","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":"2_CR10","doi-asserted-by":"crossref","unstructured":"Luo, W., Yang, B., Urtasun, R.: Fast and furious: real time end-to-end 3D detection, tracking and motion forecasting with a single convolutional net. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00376"},{"key":"2_CR11","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.cviu.2017.02.002","volume":"159","author":"M Manafifard","year":"2017","unstructured":"Manafifard, M., Ebadi, H., Moghaddam, H.A.: A survey on player tracking in soccer videos. Comput. Vis. Image Underst. 159, 19\u201346 (2017)","journal-title":"Comput. Vis. Image Underst."},{"key":"2_CR12","doi-asserted-by":"crossref","unstructured":"Qi, C.R., Litany, O., He, K., Guibas, L.J.: Deep hough voting for 3D object detection in point clouds. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00937"},{"key":"2_CR13","unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: CVPR (2017)"},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Qi, H., Feng, C., Cao, Z., Zhao, F., Xiao, Y.: P2B: point-to-box network for 3D object tracking in point clouds. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00636"},{"key":"2_CR15","doi-asserted-by":"publisher","first-page":"2947","DOI":"10.1109\/TIP.2019.2955239","volume":"29","author":"MM Rahman","year":"2020","unstructured":"Rahman, M.M., Tan, Y., Xue, J., Lu, K.: Recent advances in 3D object detection in the era of deep neural networks: a survey. IEEE Trans. Image Process. 29, 2947\u20132962 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"2_CR16","doi-asserted-by":"crossref","unstructured":"Shi, S., Wang, X., Li, H.: Pointrcnn: 3D object proposal generation and detection from point cloud. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00086"},{"key":"2_CR17","doi-asserted-by":"crossref","unstructured":"Su, H., Maji, S., Kalogerakis, E., Learned-Miller, E.G.: Multi-view convolutional neural networks for 3D shape recognition. In: ICCV. IEEE Computer Society (2015)","DOI":"10.1109\/ICCV.2015.114"},{"key":"2_CR18","unstructured":"Sun, P., et al.: Scalability in perception for autonomous driving: waymo open dataset. CoRR (2019)"},{"key":"2_CR19","doi-asserted-by":"publisher","first-page":"3206","DOI":"10.1109\/LRA.2020.2974392","volume":"5","author":"S Wang","year":"2020","unstructured":"Wang, S., Sun, Y., Liu, C., Liu, M.: Pointtracknet: an end-to-end network for 3-D object detection and tracking from point clouds. IEEE Rob. Autom. Lett. 5, 3206\u20133212 (2020)","journal-title":"IEEE Rob. Autom. Lett."},{"key":"2_CR20","doi-asserted-by":"crossref","unstructured":"Weng, X., Wang, J., Held, D., Kitani, K.: 3D multi-object tracking: a baseline and new evaluation metrics. In: IROS (2020)","DOI":"10.1109\/IROS45743.2020.9341164"},{"key":"2_CR21","doi-asserted-by":"crossref","unstructured":"Weng, X., Wang, Y., Man, Y., Kitani, K.M.: GNN3DMOT: graph neural network for 3D multi-object tracking with 2D\u20133D multi-feature learning. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00653"},{"key":"2_CR22","doi-asserted-by":"crossref","unstructured":"Yang, Y., Xu, M., Wu, W., Zhang, R., Peng, Y.: 3D multiview basketball players detection and localization based on probabilistic occupancy. In: DICTA (2018)","DOI":"10.1109\/DICTA.2018.8615798"},{"key":"2_CR23","doi-asserted-by":"crossref","unstructured":"Yang, Z., Sun, Y., Liu, S., Jia, J.: 3DSSD: point-based 3D single stage object detector. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01105"},{"key":"2_CR24","doi-asserted-by":"crossref","unstructured":"Yin, J., Shen, J., Guan, C., Zhou, D., Yang, R.: LiDAR-based online 3D video object detection with graph-based message passing and spatiotemporal transformer attention. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01151"},{"key":"2_CR25","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., Ren, D.: Distance-IoU loss: faster and better learning for bounding box regression. In: AAAI (2020)","DOI":"10.1609\/aaai.v34i07.6999"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-88004-0_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T00:26:41Z","timestamp":1792628801000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/chapter\/10.1007\/978-3-030-88004-0_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030880033","9783030880040"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-88004-0_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"22 October 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Beijing","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":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.prcv.cn\/2021\/index_en.html","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":"513","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":"201","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":"39% - 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":"5","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":"There were 30 oral and 171 poster presentations at the conference.","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)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}