{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T03:25:56Z","timestamp":1743045956039,"version":"3.40.3"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031784439"},{"type":"electronic","value":"9783031784446"}],"license":[{"start":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T00:00:00Z","timestamp":1733270400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T00:00:00Z","timestamp":1733270400000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-78444-6_19","type":"book-chapter","created":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T10:37:25Z","timestamp":1733222245000},"page":"283-298","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AFMA-Track: Adaptive Fusion of\u00a0Motion and\u00a0Appearance for\u00a0Robust Multi-object Tracking"],"prefix":"10.1007","author":[{"given":"Wei","family":"Liao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunyuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,4]]},"reference":[{"key":"19_CR1","doi-asserted-by":"crossref","unstructured":"Cao, J., Pang, J., Weng, X.: Observation-centric sort: rethinking sort for robust multi-object tracking. In: CVPR, pp. 9686\u20139696 (2023)","DOI":"10.1109\/CVPR52729.2023.00934"},{"key":"19_CR2","doi-asserted-by":"crossref","unstructured":"Qin, Z., Zhou, S., Wang, L.: MotionTrack: learning robust short-term and long-term motions for multi-object tracking. In: CVPR, pp. 17939\u201317948 (2023)","DOI":"10.1109\/CVPR52729.2023.01720"},{"key":"19_CR3","doi-asserted-by":"crossref","unstructured":"Ma, C., Yang, C., et\u00a0al.: Trajectory factory: tracklet cleaving and re-connection by deep siamese Bi-GRU for multiple object tracking. In: IEEE ICME, pp. 1\u20136 (2018)","DOI":"10.1109\/ICME.2018.8486454"},{"key":"19_CR4","doi-asserted-by":"crossref","unstructured":"Ren, H., Han, S., Ding, H.: Focus on details: online multi-object tracking with diverse fine-grained representation. In: CVPR, pp. 11289\u201311298 (2023)","DOI":"10.1109\/CVPR52729.2023.01086"},{"key":"19_CR5","doi-asserted-by":"crossref","unstructured":"Seidenschwarz, J., Bras\u00f3, G., Serrano, V.C.: Simple cues lead to a strong multi-object tracker. In: CVPR, pp. 13813\u201313823 (2023)","DOI":"10.1109\/CVPR52729.2023.01327"},{"key":"19_CR6","doi-asserted-by":"crossref","unstructured":"Yu, E., Li, Z., Han, S.: Towards discriminative representation: multi-view trajectory contrastive learning for online multi-object tracking. In: CVPR, pp. 8834\u20138843 (2022)","DOI":"10.1109\/CVPR52688.2022.00863"},{"key":"19_CR7","unstructured":"Aharon, N., Orfaig, R., Bobrovsky, B.Z.: BoT-SORT: robust associations multi-pedestrian tracking. arXiv preprint arXiv:2206.14651 (2022)"},{"key":"19_CR8","unstructured":"Milan, A., Leal-Taix\u00e9, L., Reid, I.: MOT16: a benchmark for multi-object tracking. arXiv preprint arXiv:1603.00831 (2016)"},{"key":"19_CR9","unstructured":"Dendorfer, P., Rezatofighi, H., Milan, A.: MOT20: a benchmark for multi object tracking in crowded scenes. arXiv preprint arXiv:2003.09003 (2020)"},{"key":"19_CR10","doi-asserted-by":"crossref","unstructured":"Bewley, A., Ge, Z., Ott, L.: Simple online and realtime tracking. In: ICIP, pp. 3464\u20133468 (2016)","DOI":"10.1109\/ICIP.2016.7533003"},{"key":"19_CR11","doi-asserted-by":"crossref","unstructured":"Kalman, R.E.: A new approach to linear filtering and prediction problem (1960)","DOI":"10.1115\/1.3662552"},{"issue":"1\u20132","key":"19_CR12","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. Nav. Res. Logist. Q. 2(1\u20132), 83\u201397 (1955)","journal-title":"Nav. Res. Logist. Q."},{"key":"19_CR13","unstructured":"Girbau, A., Gir\u00f3-i Nieto, X., Rius, I.: Multiple object tracking with mixture density networks for trajectory estimation. arXiv preprint arXiv:2106.10950 (2021)"},{"key":"19_CR14","doi-asserted-by":"crossref","unstructured":"Saleh, F., Aliakbarian, S., Rezatofighi, H.: Probabilistic tracklet scoring and inpainting for multiple object tracking. In: CVPR, pp. 14329\u201314339 (2021)","DOI":"10.1109\/CVPR46437.2021.01410"},{"key":"19_CR15","doi-asserted-by":"crossref","unstructured":"Cho, K., et al.: Learning phrase representations using RNN encoder\u2013decoder for statistical machine translation, pp. 1724\u20131734 (2014)","DOI":"10.3115\/v1\/D14-1179"},{"key":"19_CR16","doi-asserted-by":"crossref","unstructured":"Wojke, N., Bewley, A., Paulus, D.: Simple online and realtime tracking with a deep association metric. In: ICIP, pp. 3645\u20133649 (2017)","DOI":"10.1109\/ICIP.2017.8296962"},{"key":"19_CR17","doi-asserted-by":"crossref","unstructured":"Chen, L., Ai, H., et\u00a0al.: Real-time multiple people tracking with deeply learned candidate selection and person re-identification. In: IEEE ICME, pp. 1\u20136 (2018)","DOI":"10.1109\/ICME.2018.8486597"},{"key":"19_CR18","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.: FairMOT: on the fairness of detection and re-identification in multiple object tracking. IJCV 129, 3069\u20133087 (2021)","journal-title":"IJCV"},{"key":"19_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-031-20047-2_1","volume-title":"ECCV 2022","author":"Y Zhang","year":"2022","unstructured":"Zhang, Y., et al.: ByteTrack: multi-object tracking by associating every detection box. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13682, pp. 1\u201321. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-20047-2_1"},{"key":"19_CR20","doi-asserted-by":"crossref","unstructured":"Maggiolino, G., Ahmad, A., Cao, J., Kitani, K.: Deep oc-sort: multi-pedestrian tracking by adaptive re-identification. In: ICIP, pp. 3025\u20133029. IEEE (2023)","DOI":"10.1109\/ICIP49359.2023.10222576"},{"key":"19_CR21","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H.: Dual attention network for scene segmentation. In: CVPR, pp. 3146\u20133154 (2019)","DOI":"10.1109\/CVPR.2019.00326"},{"key":"19_CR22","doi-asserted-by":"crossref","unstructured":"Yu, E., Li, Z., Han, S.: Relationtrack: relation-aware multiple object tracking with decoupled representation. IEEE TMM (2022)","DOI":"10.1109\/TMM.2022.3150169"},{"key":"19_CR23","first-page":"3182","volume":"31","author":"C Liang","year":"2022","unstructured":"Liang, C., Zhang, Z., Zhou, X., et al.: Rethinking the competition between detection and reid in multiobject tracking. IEEE TIP 31, 3182\u20133196 (2022)","journal-title":"IEEE TIP"},{"issue":"6","key":"19_CR24","doi-asserted-by":"publisher","first-page":"7820","DOI":"10.1109\/TPAMI.2022.3225078","volume":"45","author":"Y Xu","year":"2022","unstructured":"Xu, Y., Ban, Y., et al.: TransCenter: transformers with dense representations for multiple-object tracking. IEEE TPAMI 45(6), 7820\u20137835 (2022)","journal-title":"IEEE TPAMI"},{"key":"19_CR25","unstructured":"Sun, P., Cao, J., Jiang, Y.: Transtrack: multiple object tracking with transformer. arXiv preprint arXiv:2012.15460 (2020)"},{"key":"19_CR26","doi-asserted-by":"crossref","unstructured":"Zeng, F., Dong, B., Zhang, Y.: Motr: end-to-end multiple-object tracking with transformer. In: ECCV, pp. 659\u2013675 (2022)","DOI":"10.1007\/978-3-031-19812-0_38"},{"key":"19_CR27","doi-asserted-by":"crossref","unstructured":"Du, Y., Zhao, Z., Song, Y.: Strongsort: make deepsort great again. IEEE TMM (2023)","DOI":"10.1109\/TMM.2023.3240881"},{"key":"19_CR28","doi-asserted-by":"crossref","unstructured":"You, S., Yao, H., Bao, B., Xu, C.: Utm: a unified multiple object tracking model with identity-aware feature enhancement. In: CVPR, pp. 21876\u201321886 (2023)","DOI":"10.1109\/CVPR52729.2023.02095"},{"key":"19_CR29","first-page":"1508","volume":"33","author":"Y Gao","year":"2024","unstructured":"Gao, Y., Haojun, X., Li, J., Gao, X.: Bpmtrack: multi-object tracking with detection box application pattern mining. IEEE TIP 33, 1508\u20131521 (2024)","journal-title":"IEEE TIP"},{"key":"19_CR30","doi-asserted-by":"crossref","unstructured":"Zhou, X., Koltun, V., Kr\u00e4henb\u00fchl, P.: Tracking objects as points. In: ECCV, pp. 474\u2013490 (2020)","DOI":"10.1007\/978-3-030-58548-8_28"},{"key":"19_CR31","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. EURASIP J. Image Video Process. 2008, 1\u201310 (2008)","journal-title":"EURASIP J. Image Video Process."},{"key":"19_CR32","doi-asserted-by":"publisher","first-page":"548","DOI":"10.1007\/s11263-020-01375-2","volume":"129","author":"J Luiten","year":"2021","unstructured":"Luiten, J., Osep, A., Dendorfer, P.: Hota: a higher order metric for evaluating multi-object tracking. IJCV 129, 548\u2013578 (2021)","journal-title":"IJCV"},{"key":"19_CR33","doi-asserted-by":"crossref","unstructured":"Kristan, M., Leonardis, A., Matas, J., Felsberg, M., Pflugfelder, R., Cehovin, L.: Performance measures and a data set for multi-target, multi-camera tracking. In: ECCV, pp. 17\u201335 (2016)","DOI":"10.1007\/978-3-319-48881-3_2"},{"key":"19_CR34","doi-asserted-by":"crossref","unstructured":"Lee, S.-H., Park, D.-H., Bae, S.-H.: Decode-mot: how can we hurdle frames to go beyond tracking-by-detection? IEEE TIP (2023)","DOI":"10.1109\/TIP.2023.3298538"},{"key":"19_CR35","doi-asserted-by":"crossref","unstructured":"Stadler, D., Beyerer, J.: Modelling ambiguous assignments for multi-person tracking in crowds. In: WACV, pp. 133\u2013142. IEEE (2022)","DOI":"10.1109\/WACVW54805.2022.00019"},{"key":"19_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2020.104091","volume":"106","author":"F Yang","year":"2021","unstructured":"Yang, F., Chang, X., Sakti, S., Yang, W., Nakamura, S.: Remot: a model-agnostic refinement for multiple object tracking. Image Vis. Comput. 106, 104091 (2021)","journal-title":"Image Vis. Comput."},{"key":"19_CR37","doi-asserted-by":"crossref","unstructured":"Fischer, T., et al.: Qdtrack: quasi-dense similarity learning for appearance-only multiple object tracking. IEEE TPAMI (2023)","DOI":"10.1109\/TPAMI.2023.3301975"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78444-6_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T11:36:13Z","timestamp":1733225773000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78444-6_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,4]]},"ISBN":["9783031784439","9783031784446"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78444-6_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,12,4]]},"assertion":[{"value":"4 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kolkata","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpr2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpr2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}