{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T12:04:34Z","timestamp":1777291474867,"version":"3.51.4"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030585198","type":"print"},{"value":"9783030585204","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-58520-4_6","type":"book-chapter","created":{"date-parts":[[2020,11,18]],"date-time":"2020-11-18T10:08:18Z","timestamp":1605694098000},"page":"88-104","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Count- and Similarity-Aware R-CNN for Pedestrian Detection"],"prefix":"10.1007","author":[{"given":"Jin","family":"Xie","sequence":"first","affiliation":[]},{"given":"Hisham","family":"Cholakkal","sequence":"additional","affiliation":[]},{"given":"Rao","family":"Muhammad Anwer","sequence":"additional","affiliation":[]},{"given":"Fahad","family":"Shahbaz\u00a0Khan","sequence":"additional","affiliation":[]},{"given":"Yanwei","family":"Pang","sequence":"additional","affiliation":[]},{"given":"Ling","family":"Shao","sequence":"additional","affiliation":[]},{"given":"Mubarak","family":"Shah","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,19]]},"reference":[{"key":"6_CR1","doi-asserted-by":"crossref","unstructured":"Bodla, N., Singh, B., Chellappa, R., Davis, L.S.: Soft-NMS - improving object detection with one line of code. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.593"},{"key":"6_CR2","doi-asserted-by":"crossref","unstructured":"Brazil, G., Yin, X., Liu, X.: Illuminating pedestrians via simultaneous detection & segmentation. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.530"},{"key":"6_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1007\/978-3-319-46493-0_22","volume-title":"Computer Vision \u2013 ECCV 2016","author":"Z Cai","year":"2016","unstructured":"Cai, Z., Fan, Q., Feris, R.S., Vasconcelos, N.: A unified multi-scale deep convolutional neural network for fast object detection. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 354\u2013370. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_22"},{"key":"6_CR4","doi-asserted-by":"crossref","unstructured":"Cai, Z., Vasconcelos, N.: Cascade r-cnn: High quality object detection and instance segmentation. arXiv preprint arXiv:1906.09756 (2019)","DOI":"10.1109\/CVPR.2018.00644"},{"key":"6_CR5","doi-asserted-by":"publisher","first-page":"3143","DOI":"10.1109\/TIP.2019.2957927","volume":"29","author":"J Cao","year":"2020","unstructured":"Cao, J., Pang, Y., Han, J., Gao, B., Li, X.: Taking a look at small-scale pedestrians and occluded pedestrians. IEEE Trans. Image Process. 29, 3143\u20133152 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"6_CR6","doi-asserted-by":"publisher","first-page":"3372","DOI":"10.1109\/TCSVT.2019.2950526","volume":"30","author":"J Cao","year":"2019","unstructured":"Cao, J., Pang, Y., Zhao, S., Li, X.: High-level semantic networks for multi-scale object detection. IEEE Trans. Circ. Syst. Video Technol. 30, 3372\u20133386 (2019)","journal-title":"IEEE Trans. Circ. Syst. Video Technol."},{"key":"6_CR7","doi-asserted-by":"crossref","unstructured":"Chi, C., Zhang, S., Xing, J., Lei, Z., Li, S.Z.X.Z.: Pedhunter: occlusion robust pedestrian detector in crowded scenes. In: AAAI (2020)","DOI":"10.1609\/aaai.v34i07.6690"},{"key":"6_CR8","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1109\/TPAMI.2011.155","volume":"34","author":"P Dollar","year":"2012","unstructured":"Dollar, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: an evaluation of the state of the art. TPAMI 34, 743\u2013761 (2012)","journal-title":"TPAMI"},{"key":"6_CR9","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"6_CR10","doi-asserted-by":"crossref","unstructured":"Hosang, J., Benenson, R., Schiele, B.: Learning non-maximum suppression. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.685"},{"key":"6_CR11","doi-asserted-by":"crossref","unstructured":"Hu, H., Gu, J., Zhang, Z., Dai, J., Wei, Y.: Relation networks for object detection. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00378"},{"key":"6_CR12","doi-asserted-by":"crossref","unstructured":"Jiang, B., Luo, R., Mao, J., Xiao, T., Jiang, Y.: Acquisition of localization confidence for accurate object detection. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01264-9_48"},{"key":"6_CR13","doi-asserted-by":"crossref","unstructured":"Liu, S., Huang, D., Wang, Y.: Adaptive NMS: refining pedestrian detection in a crowd. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00662"},{"key":"6_CR14","doi-asserted-by":"crossref","unstructured":"Liu, W., Liao, S., Hu, W., Liang, X., Chen, X.: Learning efficient single-stage pedestrian detectors by asymptotic localization fitting. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01264-9_38"},{"key":"6_CR15","doi-asserted-by":"crossref","unstructured":"Liu, W., Liao, S., Ren, W., Hu, W., Yu, Y.: High-level semantic feature detection: a new perspective for pedestrian detection. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00533"},{"key":"6_CR16","doi-asserted-by":"crossref","unstructured":"Mao, J., Xiao, T., Jiang, Y., Cao, Z.: What can help pedestrian detection? In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.639"},{"key":"6_CR17","doi-asserted-by":"crossref","unstructured":"Mathias, M., Benenson, R., Timofte, R., Gool, L.V.: Handling occlusions with franken-classifiers. In: ICCV (2013)","DOI":"10.1109\/ICCV.2013.190"},{"key":"6_CR18","doi-asserted-by":"crossref","unstructured":"Nie, J., Anwer, R.M., Cholakkal, H., Khan, F.S., Pang, Y., Shao, L.: Enriched feature guided refinement network for object detection. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00963"},{"key":"6_CR19","doi-asserted-by":"crossref","unstructured":"Ouyang, W., Wang, X.: Joint deep learning for pedestrian detection. In: ICCV (2013)","DOI":"10.1109\/ICCV.2013.257"},{"key":"6_CR20","doi-asserted-by":"crossref","unstructured":"Pang, Y., Xie, J., Khan, M.H., Anwer, R.M., Khan, F.S., Shao, L.: Mask-Guided attention network for occluded pedestrian detection. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00507"},{"key":"6_CR21","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NIPS (2015)"},{"key":"6_CR22","unstructured":"Shao, S., et al.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018)"},{"key":"6_CR23","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"6_CR24","doi-asserted-by":"crossref","unstructured":"Song, T., Sun, L., Xie, D., Sun, H., Pu, S.: Small-scale pedestrian detection based on topological line localization and temporal feature aggregation. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01234-2_33"},{"key":"6_CR25","doi-asserted-by":"crossref","unstructured":"Tian, Y., Luo, P., Wang, X., Tang, X.: Deep learning strong parts for pedestrian detection. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.221"},{"key":"6_CR26","doi-asserted-by":"crossref","unstructured":"Tychsen-Smith, L., Petersson, L.: Improving object localization with fitness nms and bounded IOU loss. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00719"},{"key":"6_CR27","doi-asserted-by":"crossref","unstructured":"Wang, X., Xiao, T., Jiang, Y., Shao, S., Sun, J., Shen, C.: Repulsion loss: detecting pedestrians in a crowd. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00811"},{"key":"6_CR28","doi-asserted-by":"crossref","unstructured":"Xie, J., Pang, Y., Cholakkal, H., Anwer, R.M., Khan, F.S., Shao, L.: PSC-net: learning part spatial co-occurrence for occluded pedestrian detection. arXiv preprint arXiv:2001.09252 (2020)","DOI":"10.1007\/s11432-020-2969-8"},{"key":"6_CR29","unstructured":"Zhang, J., et al.: Attribute-aware pedestrian detection in a crowd. arXiv preprint arXiv:1910.09188 (2019)"},{"key":"6_CR30","doi-asserted-by":"crossref","unstructured":"Zhang, S., Benenson, R., Schiele, B.: Citypersons: a diverse dataset for pedestrian detection. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.474"},{"key":"6_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, S., Yang, J., Schiele, B.: Occluded pedestrian detection through guided attention in CNNs. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00731"},{"key":"6_CR32","doi-asserted-by":"crossref","unstructured":"Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware R-CNN: detecting pedestrians in a crowd. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01219-9_39"},{"key":"6_CR33","doi-asserted-by":"crossref","unstructured":"Zhang, S.H., et al.: Pose2seg: detection free human instance segmentation. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00098"},{"key":"6_CR34","doi-asserted-by":"crossref","unstructured":"Zhou, C., Yang, M., Yuan, J.: Discriminative feature transformation for occluded pedestrian detection. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00965"},{"key":"6_CR35","doi-asserted-by":"crossref","unstructured":"Zhou, C., Yuan, J.: Non-rectangular part discovery for object detection. In: BMVC (2014)","DOI":"10.5244\/C.28.51"},{"key":"6_CR36","doi-asserted-by":"crossref","unstructured":"Zhou, C., Yuan, J.: Multi-label learning of part detectors for heavily occluded pedestrian detection. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.377"},{"key":"6_CR37","doi-asserted-by":"crossref","unstructured":"Zhou, C., Yuan, J.: Bi-box regression for pedestrian detection and occlusion estimation. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01246-5_9"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58520-4_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T00:16:59Z","timestamp":1731889019000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58520-4_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585198","9783030585204"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58520-4_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"19 November 2020","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":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","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":"eccv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2020.eu\/","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":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5025","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":"1360","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":"27% - 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":"7","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":"The conference was held virtually due to the COVID-19 pandemic. From the ECCV Workshops 249 full papers, 18 short papers, and 21 further contributions were published out of a total of 467 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)"}}]}}