{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T23:12:39Z","timestamp":1771542759896,"version":"3.50.1"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030505158","type":"print"},{"value":"9783030505165","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":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-50516-5_5","type":"book-chapter","created":{"date-parts":[[2020,6,19]],"date-time":"2020-06-19T17:04:00Z","timestamp":1592586240000},"page":"48-62","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["SMAT: Smart Multiple Affinity Metrics for Multiple Object Tracking"],"prefix":"10.1007","author":[{"given":"Nicolas Franco","family":"Gonzalez","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andres","family":"Ospina","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Philippe","family":"Calvez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,6,17]]},"reference":[{"issue":"9","key":"5_CR1","doi-asserted-by":"publisher","first-page":"1806","DOI":"10.1109\/TPAMI.2011.21","volume":"33","author":"J Berclaz","year":"2011","unstructured":"Berclaz, J., Fleuret, F., Turetken, E., Fua, P.: Multiple object tracking using k-shortest paths optimization. IEEE Trans. Pattern Anal. Mach. Intell. 33(9), 1806\u20131819 (2011). \nhttps:\/\/doi.org\/10.1109\/TPAMI.2011.21","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"5_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2008\/246309","volume":"2008","author":"K Bernardin","year":"2008","unstructured":"Bernardin, K., Stiefelhagen, R.: Evaluating multiple object tracking performance: the clear mot metrics. J. Image Video Process. 2008, 1 (2008)","journal-title":"J. Image Video Process."},{"key":"5_CR3","doi-asserted-by":"crossref","unstructured":"Bewley, A., Ge, Z., Ott, L., Ramos, F., Upcroft, B.: Simple online and realtime tracking. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 3464\u20133468. IEEE (2016)","DOI":"10.1109\/ICIP.2016.7533003"},{"key":"5_CR4","doi-asserted-by":"crossref","unstructured":"Bochinski, E., Eiselein, V., Sikora, T.: High-speed tracking-by-detection without using image information. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1\u20136. IEEE (2017)","DOI":"10.1109\/AVSS.2017.8078516"},{"key":"5_CR5","doi-asserted-by":"publisher","unstructured":"Choi, W.: Near-online multi-target tracking with aggregated local flow descriptor. In: 2015 IEEE International Conference on Computer Vision (ICCV), December 2015. \nhttps:\/\/doi.org\/10.1109\/iccv.2015.347\n\n, \nhttp:\/\/dx.doi.org\/10.1109\/iccv.2015.347","DOI":"10.1109\/iccv.2015.347"},{"key":"5_CR6","unstructured":"Dai, J., Li, Y., He, K., Sun, J.: R-FCN: Object detection via region-based fully convolutional networks. In: Advances in Neural Information Processing Systems, pp. 379\u2013387 (2016)"},{"key":"5_CR7","unstructured":"Dendorfer, P., et al.: CVPR19 tracking and detection challenge: how crowded can it get? \narXiv:1906.04567\n\n [cs], June 2019, \narXiv: 1906.04567"},{"key":"5_CR8","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the kitti vision benchmark suite. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2012)","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"5_CR9","doi-asserted-by":"publisher","unstructured":"G\u00fcnd\u00fcz, G., Acarman, T.: A lightweight online multiple object vehicle tracking method. In: 2018 IEEE Intelligent Vehicles Symposium (IV). pp. 427\u2013432, June 2018. \nhttps:\/\/doi.org\/10.1109\/IVS.2018.8500386","DOI":"10.1109\/IVS.2018.8500386"},{"key":"5_CR10","doi-asserted-by":"crossref","unstructured":"Huang, J., et al.: Speed\/accuracy trade-offs for modern convolutional object detectors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7310\u20137311 (2017)","DOI":"10.1109\/CVPR.2017.351"},{"key":"5_CR11","unstructured":"Kanade, L.: An iterative image registration technique with an application to stereo vision"},{"key":"5_CR12","doi-asserted-by":"publisher","first-page":"104423","DOI":"10.1109\/ACCESS.2019.2932301","volume":"7","author":"H Karunasekera","year":"2019","unstructured":"Karunasekera, H., Wang, H., Zhang, H.: Multiple object tracking with attention to appearance, structure, motion and size. IEEE Access 7, 104423\u2013104434 (2019). \nhttps:\/\/doi.org\/10.1109\/ACCESS.2019.2932301","journal-title":"IEEE Access"},{"key":"5_CR13","doi-asserted-by":"publisher","unstructured":"Kim, C., Li, F., Ciptadi, A., Rehg, J.M.: Multiple hypothesis tracking revisited. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 4696\u20134704, December 2015. \nhttps:\/\/doi.org\/10.1109\/ICCV.2015.533","DOI":"10.1109\/ICCV.2015.533"},{"issue":"1\u20132","key":"5_CR14","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1002\/nav.3800020109","volume":"2","author":"HW Kuhn","year":"1955","unstructured":"Kuhn, H.W.: The hungarian method for the assignment problem. Naval Res. Logistics Quarterly 2(1\u20132), 83\u201397 (1955)","journal-title":"Naval Res. Logistics Quarterly"},{"key":"5_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1007\/978-3-319-48881-3_6","volume-title":"Computer Vision \u2013 ECCV 2016 Workshops","author":"B Lee","year":"2016","unstructured":"Lee, B., Erdenee, E., Jin, S., Nam, M.Y., Jung, Y.G., Rhee, P.K.: Multi-class multi-object tracking using changing point detection. In: Hua, G., J\u00e9gou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 68\u201383. Springer, Cham (2016). \nhttps:\/\/doi.org\/10.1007\/978-3-319-48881-3_6"},{"key":"5_CR16","doi-asserted-by":"crossref","unstructured":"Li, W., Zhao, R., Xiao, T., Wang, X.: Deepreid: deep filter pairing neural network for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 152\u2013159 (2014)","DOI":"10.1109\/CVPR.2014.27"},{"key":"5_CR17","doi-asserted-by":"publisher","unstructured":"Li, Y., Huang, C., Nevatia, R.: Learning to associate: hybridboosted multi-target tracker for crowded scene. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2953\u20132960, June 2009. \nhttps:\/\/doi.org\/10.1109\/CVPR.2009.5206735","DOI":"10.1109\/CVPR.2009.5206735"},{"key":"5_CR18","doi-asserted-by":"publisher","unstructured":"Zhang, L., Li, Y., Nevatia, R.: Global data association for multi-object tracking using network flows. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20138, June 2008. \nhttps:\/\/doi.org\/10.1109\/CVPR.2008.4587584","DOI":"10.1109\/CVPR.2008.4587584"},{"key":"5_CR19","doi-asserted-by":"crossref","unstructured":"Liu, X., Liu, W., Ma, H., Fu, H.: Large-scale vehicle re-identification in urban surveillance videos. In: 2016 IEEE International Conference on Multimedia and Expo (ICME), pp. 1\u20136, July 2016","DOI":"10.1109\/ICME.2016.7553002"},{"key":"5_CR20","doi-asserted-by":"crossref","unstructured":"Lyu, S., et al.: UA-DETRAC 2017: Report of AVSS2017 & IWT4S challenge on advanced traffic monitoring. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1\u20137. IEEE (2017)","DOI":"10.1109\/AVSS.2017.8078560"},{"issue":"1","key":"5_CR21","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1109\/TPAMI.2013.103","volume":"36","author":"A Milan","year":"2014","unstructured":"Milan, A., Roth, S., Schindler, K.: Continuous energy minimization for multitarget tracking. IEEE Trans. Pattern Anal. Mach. Intell. 36(1), 58\u201372 (2014). \nhttps:\/\/doi.org\/10.1109\/TPAMI.2013.103","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"5_CR22","doi-asserted-by":"crossref","unstructured":"Murthy, J.K., Sharma, S., Krishna, K.M.: Shape priors for real-time monocular object localization in dynamic environments. In: 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1768\u20131774. IEEE (2017)","DOI":"10.1109\/IROS.2017.8205990"},{"key":"5_CR23","unstructured":"Ren, J., et al.: Accurate single stage detector using recurrent rolling convolution. \nhttp:\/\/arxiv.org\/abs\/1704.05776"},{"key":"5_CR24","first-page":"91","volume":"36","author":"S Ren","year":"2015","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. Adv. Neural Inf. Process. Syst. 36, 91\u201399 (2015)","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"1","key":"5_CR25","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1109\/TPAMI.2008.275","volume":"32","author":"E Rosten","year":"2008","unstructured":"Rosten, E., Porter, R., Drummond, T.: Faster and better: a machine learning approach to corner detection. IEEE Trans. Pattern Anal. Mach. Intell. 32(1), 105\u2013119 (2008)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"5_CR26","doi-asserted-by":"crossref","unstructured":"Sharma, S., Ansari, J.A., Murthy, J.K., Krishna, K.M.: Beyond pixels: leveraging geometry and shape cues for online multi-object tracking. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 3508\u20133515. IEEE (2018)","DOI":"10.1109\/ICRA.2018.8461018"},{"key":"5_CR27","unstructured":"Shi, J., et al.: Good features to track. In: 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 593\u2013600. IEEE (1994)"},{"key":"5_CR28","first-page":"1","volume":"39","author":"W Tian","year":"2019","unstructured":"Tian, W., Lauer, M., Chen, L.: Online multi-object tracking using joint domain information in traffic scenarios. IEEE Trans. Intell. Transport. Syst. 39, 1\u201311 (2019)","journal-title":"IEEE Trans. Intell. Transport. Syst."},{"key":"5_CR29","doi-asserted-by":"crossref","unstructured":"Wang, G., Yuan, Y., Chen, X., Li, J., Zhou, X.: Learning discriminative features with multiple granularities for person re-identification. In: 2018 ACM Multimedia Conference on Multimedia Conference, pp. 274\u2013282. ACM (2018)","DOI":"10.1145\/3240508.3240552"},{"key":"5_CR30","doi-asserted-by":"crossref","unstructured":"Wojke, N., Bewley, A., Paulus, D.: Simple online and realtime tracking with a deep association metric. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 3645\u20133649. IEEE (2017)","DOI":"10.1109\/ICIP.2017.8296962"},{"key":"5_CR31","doi-asserted-by":"crossref","unstructured":"Xiang, Y., Alahi, A., Savarese, S.: Learning to track: online multi-object tracking by decision making. In: The IEEE International Conference on Computer Vision (ICCV), December 2015","DOI":"10.1109\/ICCV.2015.534"},{"key":"5_CR32","doi-asserted-by":"crossref","unstructured":"Yang, F., Choi, W., Lin, Y.: Exploit all the layers: fast and accurate CNN object detector with scale dependent pooling and cascaded rejection classifiers. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016","DOI":"10.1109\/CVPR.2016.234"},{"key":"5_CR33","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1007\/978-3-319-48881-3_3","volume-title":"Computer Vision \u2013 ECCV 2016 Workshops","author":"F Yu","year":"2016","unstructured":"Yu, F., Li, W., Li, Q., Liu, Y., Shi, X., Yan, J.: POI: multiple object tracking with high performance detection and appearance feature. In: Hua, G., J\u00e9gou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 36\u201342. Springer, Cham (2016). \nhttps:\/\/doi.org\/10.1007\/978-3-319-48881-3_3"}],"container-title":["Lecture Notes in Computer Science","Image Analysis and Recognition"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-50516-5_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,6,19]],"date-time":"2020-06-19T17:14:39Z","timestamp":1592586879000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-50516-5_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030505158","9783030505165"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-50516-5_5","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":"17 June 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIAR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image Analysis and Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"P\u00f3voa de Varzim","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","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":"24 June 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 June 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iciar2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.aimiconf.org\/iciar20\/","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":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"123","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":"54","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":"15","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":"44% - 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":"2,9","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":"3,8","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":"Due to the corona pandemic, ICIAR 2020 will be held virtually only.","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)"}}]}}