{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T17:01:12Z","timestamp":1777568472775,"version":"3.51.4"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031263187","type":"print"},{"value":"9783031263194","type":"electronic"}],"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\/978-3-031-26319-4_2","type":"book-chapter","created":{"date-parts":[[2023,3,3]],"date-time":"2023-03-03T06:02:59Z","timestamp":1677823379000},"page":"20-35","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Temporal-Aware Siamese Tracker: Integrate Temporal Context for\u00a03D Object Tracking"],"prefix":"10.1007","author":[{"given":"Kaihao","family":"Lan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haobo","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,4]]},"reference":[{"key":"2_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1007\/978-3-319-48881-3_56","volume-title":"Computer Vision \u2013 ECCV 2016 Workshops","author":"L Bertinetto","year":"2016","unstructured":"Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., Torr, P.H.S.: Fully-convolutional Siamese networks for object tracking. In: Hua, G., J\u00e9gou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 850\u2013865. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-48881-3_56"},{"key":"2_CR2","doi-asserted-by":"crossref","unstructured":"Bibi, A., Zhang, T., Ghanem, B.: 3D part-based sparse tracker with automatic synchronization and registration. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.160"},{"key":"2_CR3","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":"2_CR4","doi-asserted-by":"crossref","unstructured":"Comport, A.I., Marchand, \u00c9., Chaumette, F.: Robust model-based tracking for robot vision. In: IROS (2004)","DOI":"10.1163\/156855305774662226"},{"key":"2_CR5","unstructured":"Cui, Y., Fang, Z., Shan, J., Gu, Z., Zhou, S.: 3D object tracking with transformer. arXiv preprint arXiv:2110.14921 (2021)"},{"key":"2_CR6","doi-asserted-by":"crossref","unstructured":"Fang, Z., Zhou, S., Cui, Y., Scherer, S.: 3D-SiamRPN: an end-to-end learning method for real-time 3d single object tracking using raw point cloud. IEEE Sens. J. (2020)","DOI":"10.1109\/JSEN.2020.3033034"},{"key":"2_CR7","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_CR8","doi-asserted-by":"crossref","unstructured":"Giancola, S., Zarzar, J., Ghanem, B.: Leveraging shape completion for 3D Siamese tracking. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00145"},{"key":"2_CR9","doi-asserted-by":"crossref","unstructured":"Guo, Q., Feng, W., Zhou, C., Huang, R., Wan, L., Wang, S.: Learning dynamic Siamese network for visual object tracking. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.196"},{"key":"2_CR10","unstructured":"Hui, L., Wang, L., Cheng, M., Xie, J., Yang, J.: 3D Siamese voxel-to-BEV tracker for sparse point clouds. In: NeurIPS (2021)"},{"key":"2_CR11","doi-asserted-by":"publisher","unstructured":"Hui, L., Wang, L., Tang, L., Lan, K., Xie, J., Yang, J.: 3D Siamese transformer network for single object tracking on point clouds. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds) Computer Vision \u2013 ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol. 13662, pp. 293\u2013310. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-20086-1_17","DOI":"10.1007\/978-3-031-20086-1_17"},{"key":"2_CR12","doi-asserted-by":"crossref","unstructured":"Jiang, H., Lan, K., Hui, L., Li, G., Xie, J., Yang, J.: Point cloud registration-driven robust feature matching for 3d siamese object tracking. arXiv preprint arXiv:2209.06395 (2022)","DOI":"10.1109\/TNNLS.2023.3325286"},{"key":"2_CR13","unstructured":"Katharopoulos, A., Vyas, A., Pappas, N., Fleuret, F.: Transformers are RNNs: fast autoregressive transformers with linear attention. In: ICML (2020)"},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Kelly, A.: A 3d state space formulation of a navigation Kalman filter for autonomous vehicles. Carnegie-Mellon Univ Pittsburgh Pa Robotics Inst, Technical report (1994)","DOI":"10.21236\/ADA282853"},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"Kim, A., O\u0161ep, A., Leal-Taix\u00e9, L.: EagerMOT: 3D multi-object tracking via sensor fusion. In: ICRA (2021)","DOI":"10.1109\/ICRA48506.2021.9562072"},{"key":"2_CR16","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)"},{"key":"2_CR17","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_CR18","doi-asserted-by":"crossref","unstructured":"Luo, C., Yang, X., Yuille, A.: Exploring simple 3D multi-object tracking for autonomous driving. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.01032"},{"key":"2_CR19","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_CR20","doi-asserted-by":"crossref","unstructured":"Pang, Z., Li, Z., Wang, N.: Model-free vehicle tracking and state estimation in point cloud sequences. In: IROS (2021)","DOI":"10.1109\/IROS51168.2021.9636202"},{"key":"2_CR21","unstructured":"Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: NeurIPS (2019)"},{"key":"2_CR22","doi-asserted-by":"crossref","unstructured":"Pieropan, A., Bergstr\u00f6m, N., Ishikawa, M., Kjellstr\u00f6m, H.: Robust 3D tracking of unknown objects. In: ICRA (2015)","DOI":"10.1109\/ICRA.2015.7139520"},{"key":"2_CR23","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_CR24","unstructured":"Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNet++: deep hierarchical feature learning on point sets in a metric space. In: NeurIPS (2017)"},{"key":"2_CR25","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_CR26","doi-asserted-by":"crossref","unstructured":"Scheidegger, S., Benjaminsson, J., Rosenberg, E., Krishnan, A., Granstr\u00f6m, K.: Mono-camera 3D multi-object tracking using deep learning detections and PMBM filtering. In: IV (2018)","DOI":"10.1109\/IVS.2018.8500454"},{"key":"2_CR27","doi-asserted-by":"crossref","unstructured":"Shan, J., Zhou, S., Fang, Z., Cui, Y.: PTT: Point-track-transformer module for 3D single object tracking in point clouds. In: IROS (2021)","DOI":"10.1109\/IROS51168.2021.9636821"},{"key":"2_CR28","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_CR29","doi-asserted-by":"crossref","unstructured":"Sun, P., et al.: Scalability in perception for autonomous driving: WAYMO open dataset. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00252"},{"key":"2_CR30","doi-asserted-by":"crossref","unstructured":"Tao, R., Gavves, E., Smeulders, A.W.: Siamese instance search for tracking. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.158"},{"key":"2_CR31","unstructured":"Vaswani, A., et al.: Attention is all you need. arXiv preprint arXiv:1706.03762 (2017)"},{"key":"2_CR32","doi-asserted-by":"crossref","unstructured":"Wang, L., Hui, L., Xie, J.: Facilitating 3D object tracking in point clouds with image semantics and geometry. In: PRCV (2021)","DOI":"10.1007\/978-3-030-88004-0_48"},{"key":"2_CR33","doi-asserted-by":"crossref","unstructured":"Wang, N., Zhou, W., Wang, J., Li, H.: Transformer meets tracker: exploiting temporal context for robust visual tracking. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00162"},{"key":"2_CR34","unstructured":"Weng, X., Kitani, K.: A baseline for 3D multi-object tracking. arXiv preprint arXiv:1907.03961 (2019)"},{"key":"2_CR35","doi-asserted-by":"publisher","first-page":"5668","DOI":"10.1109\/TITS.2021.3055616","volume":"23","author":"H Wu","year":"2021","unstructured":"Wu, H., Han, W., Wen, C., Li, X., Wang, C.: 3D multi-object tracking in point clouds based on prediction confidence-guided data association. IEEE Trans. Intell. Transp. Syst. 23, 5668\u20135677 (2021)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"2_CR36","doi-asserted-by":"crossref","unstructured":"Yin, T., Zhou, X., Krahenbuhl, P.: Center-based 3d object detection and tracking. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01161"},{"key":"2_CR37","doi-asserted-by":"crossref","unstructured":"Zheng, C., et al.: Box-aware feature enhancement for single object tracking on point clouds. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.01295"},{"key":"2_CR38","doi-asserted-by":"crossref","unstructured":"Zhou, C., et al.: PTTR: relational 3D point cloud object tracking with transformer. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00834"},{"key":"2_CR39","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1007\/978-3-030-01240-3_7","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Z Zhu","year":"2018","unstructured":"Zhu, Z., Wang, Q., Li, B., Wu, W., Yan, J., Hu, W.: Distractor-aware Siamese networks for visual object tracking. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11213, pp. 103\u2013119. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01240-3_7"}],"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\/978-3-031-26319-4_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,8]],"date-time":"2023-12-08T05:44:39Z","timestamp":1702014279000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-26319-4_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031263187","9783031263194"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-26319-4_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"4 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)"}}]}}