{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T06:22:57Z","timestamp":1743142977667,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":29,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819981809"},{"type":"electronic","value":"9789819981816"}],"license":[{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-8181-6_15","type":"book-chapter","created":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T23:02:30Z","timestamp":1701039750000},"page":"191-204","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["BFTracker: A One-Shot Baseline Model with\u00a0Fusion Similarity Algorithm Towards Real-Time Multi-object Tracking"],"prefix":"10.1007","author":[{"given":"Fuxiao","family":"He","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoqiang","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,27]]},"reference":[{"key":"15_CR1","unstructured":"Zhou, X., et al.: Objects as points. In: arXiv:1904.07850 (2019)"},{"key":"15_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1007\/978-3-030-58548-8_28","volume-title":"Computer Vision \u2013 ECCV 2020","author":"X Zhou","year":"2020","unstructured":"Zhou, X., Koltun, V., Kr\u00e4henb\u00fchl, P.: Tracking objects as points. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12349, pp. 474\u2013490. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58548-8_28"},{"issue":"11","key":"15_CR3","doi-asserted-by":"publisher","first-page":"3069","DOI":"10.1007\/s11263-021-01513-4","volume":"129","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., Wang, C., Wang, X., Zeng, W., Liu, W.: FairMOT: on the fairness of detection and re-identification in multiple object tracking. Int. J. Comput. Vis. 129(11), 3069\u20133087 (2021). https:\/\/doi.org\/10.1007\/s11263-021-01513-4","journal-title":"Int. J. Comput. Vis."},{"key":"15_CR4","doi-asserted-by":"publisher","first-page":"83085","DOI":"10.1109\/ACCESS.2022.3197157","volume":"10","author":"R Mostafa","year":"2022","unstructured":"Mostafa, R., et al.: LMOT: efficient light-weight detection and tracking in crowds. IEEE Access 10, 83085\u201383095 (2022)","journal-title":"IEEE Access"},{"key":"15_CR5","doi-asserted-by":"crossref","unstructured":"Dai, J., et al.: Deformable convolutional networks. In: International Conference on Computer Vision (ICCV), pp. 764\u2013773 (2017)","DOI":"10.1109\/ICCV.2017.89"},{"key":"15_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/978-3-030-58621-8_7","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Z Wang","year":"2020","unstructured":"Wang, Z., Zheng, L., Liu, Y., Li, Y., Wang, S.: Towards real-time multi-object tracking. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12356, pp. 107\u2013122. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58621-8_7"},{"key":"15_CR7","doi-asserted-by":"crossref","unstructured":"Lin, T., et al.: Feature pyramid networks for object detection. In: Computer Vision and Pattern Recognition (CVPR), pp. 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"15_CR8","unstructured":"Milan, A., et al.: MOT16: a benchmark for multi-object tracking. In: arXiv: 1603.00831 (2016)"},{"key":"15_CR9","doi-asserted-by":"crossref","unstructured":"Dendorfer, P., et al.: MOTChallenge: a benchmark for single-camera multiple target tracking. In: International Journal of Computer Vision (IJCV) 129.4, pp. 845\u2013881 (2021)","DOI":"10.1007\/s11263-020-01393-0"},{"key":"15_CR10","doi-asserted-by":"crossref","unstructured":"Wang, C., et al.: YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: arXiv: 2207.02696 (2022)","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"15_CR11","doi-asserted-by":"crossref","unstructured":"Tan, M., et al.: EfficientDet: scalable and efficient object detection. In: Computer Vision and Pattern Recognition (CVPR), pp. 10781\u201310790 (2020)","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"15_CR12","doi-asserted-by":"crossref","unstructured":"Lin, T, et al.: Focal loss for dense object detection. In: International Conference on Computer Vision (ICCV), pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"15_CR13","doi-asserted-by":"crossref","unstructured":"Chen, L., et al.: Real-time multiple people tracking with deeply learned candidate selection and person re-identification. In: International Conference on Multimedia and Expo (ICME), pp. 1\u20136 (2018)","DOI":"10.1109\/ICME.2018.8486597"},{"key":"15_CR14","doi-asserted-by":"publisher","unstructured":"Zhang, Y., et al.: ByteTrack: multi-object tracking by associating every detection box. In: European Conference on Computer Vision (ECCV), pp. 1\u201321. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-20047-2_1","DOI":"10.1007\/978-3-031-20047-2_1"},{"key":"15_CR15","doi-asserted-by":"crossref","unstructured":"Kuhn, H.: The Hungarian method for the assignment problem. In: Naval research logistics quarterly 2.1-2, pp. 83\u201397 (1955)","DOI":"10.1002\/nav.3800020109"},{"key":"15_CR16","doi-asserted-by":"crossref","unstructured":"Zhang, S., et al.: CityPersons: a diverse dataset for pedestrian detection. In: Computer Vision and Pattern Recognition (CVPR), pp. 3213\u20133221 (2017)","DOI":"10.1109\/CVPR.2017.474"},{"key":"15_CR17","doi-asserted-by":"crossref","unstructured":"Xiao, T., et al.: Joint detection and identification feature learning for person search. In: Computer Vision and Pattern Recognition (CVPR), pp. 3415\u20133424 (2017)","DOI":"10.1109\/CVPR.2017.360"},{"key":"15_CR18","doi-asserted-by":"crossref","unstructured":"Doll\u00e1r, P., et al.: Pedestrian detection: a benchmark. In: Computer Vision and Pattern Recognition (CVPR), pp. 304\u2013311 (2009)","DOI":"10.1109\/CVPR.2009.5206631"},{"key":"15_CR19","doi-asserted-by":"crossref","unstructured":"Ess, A., et al.: A mobile vision system for robust multi-person tracking. In: Computer Vision and Pattern Recognition (CVPR), pp. 1\u20138 (2008)","DOI":"10.1109\/CVPR.2008.4587581"},{"key":"15_CR20","doi-asserted-by":"crossref","unstructured":"Zheng, L., et al.: Person re-identification in the wild. In: Computer Vision and Pattern Recognition (CVPR), pp. 1367\u20131376 (2017)","DOI":"10.1109\/CVPR.2017.357"},{"key":"15_CR21","unstructured":"Shao, S., et al.: CrowdHuman: a benchmark for detecting human in a crowd. In: arXiv: 1805.00123 (2018)"},{"key":"15_CR22","doi-asserted-by":"crossref","unstructured":"Bernardin, K., et al.: Evaluating multiple object tracking performance: the clear mot metrics. In: EURASIP J., 1\u201310 (2008)","DOI":"10.1155\/2008\/246309"},{"key":"15_CR23","doi-asserted-by":"crossref","unstructured":"Luiten, J., et al.: HOTA: a higher order metric for evaluating multi-object tracking. Int. J. Comput. Vis. (IJCV) 129.2, 548\u2013578 (2021)","DOI":"10.1007\/s11263-020-01375-2"},{"key":"15_CR24","unstructured":"Glenn, J.: www.github.com\/ultralytics"},{"key":"15_CR25","doi-asserted-by":"crossref","unstructured":"Liu, S., et al.: Path aggregation network for instance segmentation. In: Computer Vision and Pattern Recognition (CVPR), pp. 8759\u20138768 (2018)","DOI":"10.1109\/CVPR.2018.00913"},{"key":"15_CR26","doi-asserted-by":"crossref","unstructured":"Wojke, N., et al.: Simple online and realtime tracking with a deep association metric. In: International Conference on Image Processing (ICIP), pp. 3645\u20133649 (2017)","DOI":"10.1109\/ICIP.2017.8296962"},{"key":"15_CR27","unstructured":"Liang, C., et al.: Rethinking the competition between detection and Re-ID in Multi-object tracking. In: arXiv:2010.12138 (2020)"},{"key":"15_CR28","doi-asserted-by":"crossref","unstructured":"Wang, Q., et al.: Multiple object tracking with correlation learning. In: Computer Vision and Pattern Recognition (CVPR), pp. 3876\u20133886 (2021)","DOI":"10.1109\/CVPR46437.2021.00387"},{"key":"15_CR29","doi-asserted-by":"crossref","unstructured":"Liang, C., et al.: One more check: making \"fake background\" be tracked again. In: Association for the Advance of Artificial Intelligence (AAAI), vol. 36, No. 2, pp. 1546\u20131554 (2022)","DOI":"10.1609\/aaai.v36i2.20045"}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8181-6_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T11:26:59Z","timestamp":1710329219000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8181-6_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,27]]},"ISBN":["9789819981809","9789819981816"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8181-6_15","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023,11,27]]},"assertion":[{"value":"27 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changsha","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1274","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":"650","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":"51% - 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":"4.14","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":"2.46","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)"}}]}}