{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T18:41:34Z","timestamp":1745606494666,"version":"3.40.3"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031189159"},{"type":"electronic","value":"9783031189166"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-18916-6_38","type":"book-chapter","created":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T23:03:53Z","timestamp":1666825433000},"page":"464-476","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Detection Beyond What and Where: A Benchmark for\u00a0Detecting Occlusion State"],"prefix":"10.1007","author":[{"given":"Liwei","family":"Qin","sequence":"first","affiliation":[]},{"given":"Hui","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Zhongtian","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jiaqi","family":"Deng","sequence":"additional","affiliation":[]},{"given":"Yuanyuan","family":"Liao","sequence":"additional","affiliation":[]},{"given":"Shuiwang","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,27]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Chen, Q., et al.: You only look one-level feature. In: 2021 CVPR, pp. 13034\u201313043 (2021)","key":"38_CR1","DOI":"10.1109\/CVPR46437.2021.01284"},{"doi-asserted-by":"crossref","unstructured":"Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 CVPR, vol. 1, pp. 886\u2013893 (2005)","key":"38_CR2","DOI":"10.1109\/CVPR.2005.177"},{"doi-asserted-by":"crossref","unstructured":"Deng, J., et al.: ImageNet: a large-scale hierarchical image database. In: 2009 CVPR (2009)","key":"38_CR3","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"38_CR4","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1109\/TPAMI.2011.155","volume":"34","author":"P Doll\u00e1r","year":"2012","unstructured":"Doll\u00e1r, P., Wojek, C., et al.: Pedestrian detection: an evaluation of the state of the art. IEEE Trans. Pattern Anal. Mach. Intell. 34, 743\u2013761 (2012)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"38_CR5","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2009","unstructured":"Everingham, M., et al.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vision 88, 303\u2013338 (2009)","journal-title":"Int. J. Comput. Vision"},{"doi-asserted-by":"crossref","unstructured":"Feng, C., et al.: TOOD: task-aligned one-stage object detection. In: 2021 ICCV, pp. 3490\u20133499 (2021)","key":"38_CR6","DOI":"10.1109\/ICCV48922.2021.00349"},{"unstructured":"Ge, Z., et al.: YOLOX: exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430 (2021)","key":"38_CR7"},{"doi-asserted-by":"crossref","unstructured":"Girshick, R.B.: Fast R-CNN. In: 2015 ICCV, pp. 1440\u20131448 (2015)","key":"38_CR8","DOI":"10.1109\/ICCV.2015.169"},{"doi-asserted-by":"crossref","unstructured":"Girshick, R.B., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 CVPR, pp. 580\u2013587 (2014)","key":"38_CR9","DOI":"10.1109\/CVPR.2014.81"},{"doi-asserted-by":"crossref","unstructured":"He, K., et al.: Mask R-CNN. In: 2017 ICCV, pp. 2980\u20132988 (2017)","key":"38_CR10","DOI":"10.1109\/ICCV.2017.322"},{"key":"38_CR11","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","volume":"37","author":"K He","year":"2015","unstructured":"He, K., et al.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37, 1904\u20131916 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"doi-asserted-by":"crossref","unstructured":"He, K., et al.: Deep residual learning for image recognition. In: 2016 CVPR, pp. 770\u2013778 (2016)","key":"38_CR12","DOI":"10.1109\/CVPR.2016.90"},{"key":"38_CR13","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1006\/cviu.2001.0921","volume":"83","author":"E Hjelm\u00e5s","year":"2001","unstructured":"Hjelm\u00e5s, E., Low, B.K.: Face detection: a survey. Comput. Vis. Image Underst. 83, 236\u2013274 (2001)","journal-title":"Comput. Vis. Image Underst."},{"unstructured":"Huang, G.B., et al.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments (2008)","key":"38_CR14"},{"key":"38_CR15","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/j.cviu.2016.03.005","volume":"153","author":"S Lao","year":"2016","unstructured":"Lao, S., et al.: Human running detection: benchmark and baseline. Comput. Vis. Image Underst. 153, 143\u2013150 (2016)","journal-title":"Comput. Vis. Image Underst."},{"doi-asserted-by":"crossref","unstructured":"Lin, T.Y., et al.: Feature pyramid networks for object detection. In: 2017 CVPR, pp. 936\u2013944 (2017)","key":"38_CR16","DOI":"10.1109\/CVPR.2017.106"},{"key":"38_CR17","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","volume":"42","author":"TY Lin","year":"2020","unstructured":"Lin, T.Y., et al.: Focal loss for dense object detection. IEEE Trans. Pattern Anal. Mach. Intell. 42, 318\u2013327 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"doi-asserted-by":"crossref","unstructured":"Lin, T.Y., et al.: Microsoft COCO: common objects in context. In: ECCV (2014)","key":"38_CR18","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"38_CR19","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1007\/s11263-019-01247-4","volume":"128","author":"L Liu","year":"2019","unstructured":"Liu, L., et al.: Deep learning for generic object detection: a survey. Int. J. Comput. Vision 128, 261\u2013318 (2019)","journal-title":"Int. J. Comput. Vision"},{"doi-asserted-by":"crossref","unstructured":"Liu, W., et al.: SSD: Single shot multibox detector. In: ECCV (2016)","key":"38_CR20","DOI":"10.1007\/978-3-319-46448-0_2"},{"doi-asserted-by":"crossref","unstructured":"Liu, X., et al.: Benchmark for road marking detection: dataset specification and performance baseline. In: 2017 IEEE ITSC, pp. 1\u20136 (2017)","key":"38_CR21","DOI":"10.1109\/ITSC.2017.8317749"},{"doi-asserted-by":"crossref","unstructured":"Lu, V.N., et al.: Service robots, customers and service employees: what can we learn from the academic literature and where are the gaps? J. Serv. Theor. Practic. 30(3), 361\u2013391 (2020)","key":"38_CR22","DOI":"10.1108\/JSTP-04-2019-0088"},{"doi-asserted-by":"crossref","unstructured":"Najibi, M., et al.: G-CNN: an iterative grid based object detector. In: 2016 CVPR, pp. 2369\u20132377 (2016)","key":"38_CR23","DOI":"10.1109\/CVPR.2016.260"},{"unstructured":"Naud\u00e9, J.J., Joubert, D.: The aerial elephant dataset: a new public benchmark for aerial object detection. In: CVPR Workshops (2019)","key":"38_CR24"},{"doi-asserted-by":"crossref","unstructured":"Pawar, P., Devendran, V.: Scene understanding: a survey to see the world at a single glance. In: 2019 ICCT, pp. 182\u2013186 (2019)","key":"38_CR25","DOI":"10.1109\/ICCT46177.2019.8969051"},{"doi-asserted-by":"crossref","unstructured":"Redmon, J., et al.: You only look once: unified, real-time object detection. In: 2016 CVPR, pp. 779\u2013788 (2016)","key":"38_CR26","DOI":"10.1109\/CVPR.2016.91"},{"doi-asserted-by":"crossref","unstructured":"Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: 2017 CVPR, pp. 6517\u20136525 (2017)","key":"38_CR27","DOI":"10.1109\/CVPR.2017.690"},{"key":"38_CR28","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2015","unstructured":"Ren, S., He, K., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39, 1137\u20131149 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"38_CR29","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1080\/01441647.2018.1494640","volume":"39","author":"A Taeihagh","year":"2018","unstructured":"Taeihagh, A., Lim, H.S.M.: Governing autonomous vehicles: emerging responses for safety, liability, privacy, cybersecurity, and industry risks. Transp. Rev. 39, 103\u2013128 (2018)","journal-title":"Transp. Rev."},{"key":"38_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106404","volume":"189","author":"O Wosner","year":"2021","unstructured":"Wosner, O., et al.: Object detection in agricultural contexts: a multiple resolution benchmark and comparison to human. Comput. Electron. Agric. 189, 106404 (2021)","journal-title":"Comput. Electron. Agric."},{"doi-asserted-by":"crossref","unstructured":"Yoo, D., et al.: AttentionNet: aggregating weak directions for accurate object detection. In: 2015 ICCV, pp. 2659\u20132667 (2015)","key":"38_CR31","DOI":"10.1109\/ICCV.2015.305"},{"key":"38_CR32","doi-asserted-by":"publisher","first-page":"3212","DOI":"10.1109\/TNNLS.2018.2876865","volume":"30","author":"ZQ Zhao","year":"2019","unstructured":"Zhao, Z.Q., et al.: Object detection with deep learning: a review. IEEE Trans. Neural Netw. Learn. Syst. 30, 3212\u20133232 (2019)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"unstructured":"Zou, Z., et al.: Object detection in 20 years: a survey. arXiv arXiv:abs\/1905.05055 (2019)","key":"38_CR33"}],"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\/978-3-031-18916-6_38","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,6]],"date-time":"2024-10-06T16:36:50Z","timestamp":1728232610000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-18916-6_38"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031189159","9783031189166"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-18916-6_38","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"27 October 2022","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":"Shenzhen","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":"14 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/en.prcv.cn\/","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":"microsoft","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"564","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":"233","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":"41% - 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":"3.03","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":"3.35","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}