{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T04:10:21Z","timestamp":1751947821445,"version":"3.41.2"},"reference-count":97,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T00:00:00Z","timestamp":1751846400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T00:00:00Z","timestamp":1751846400000},"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":["J Supercomput"],"DOI":"10.1007\/s11227-025-07466-6","type":"journal-article","created":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T14:21:04Z","timestamp":1751898064000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Preaftrack: multi-object tracking based on adaptive feature matching from detection results"],"prefix":"10.1007","volume":"81","author":[{"given":"Mandun","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenyue","family":"Hou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yonghui","family":"Pang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiale","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiqi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangsheng","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,7]]},"reference":[{"key":"7466_CR1","doi-asserted-by":"publisher","DOI":"10.3390\/app12199408","author":"A Gad","year":"2022","unstructured":"Gad A, Basmaji T, Yaghi M, Alheeh H, Alkhedher M, Ghazal M (2022) Multiple object tracking in robotic applications: trends and challenges. Appl Sci. https:\/\/doi.org\/10.3390\/app12199408","journal-title":"Appl Sci"},{"key":"7466_CR2","doi-asserted-by":"publisher","unstructured":"Said T, Ghoniemy S, Karam O (2012) Real-time multi-object detection and tracking for autonomous robots in uncontrolled environments. In: 2012 Seventh International Conference on Computer Engineering & Systems (ICCES), pp. 67\u201372. https:\/\/doi.org\/10.1109\/ICCES.2012.6408485","DOI":"10.1109\/ICCES.2012.6408485"},{"key":"7466_CR3","doi-asserted-by":"crossref","unstructured":"Chiu H.-k, Prioletti A, Li J, Bohg J (2020) Probabilistic 3d multi-object tracking for autonomous driving. arXiv:abs\/2001.05673","DOI":"10.1109\/ICRA48506.2021.9561754"},{"key":"7466_CR4","doi-asserted-by":"crossref","unstructured":"Guo S, Wang S, Yang Z, Wang L, Zhang H, Guo P, Gao Y, Guo J (2022) A review of deep learning-based visual multi-object tracking algorithms for autonomous driving. Appl Sci","DOI":"10.3390\/app122110741"},{"key":"7466_CR5","unstructured":"Pang Z, Li Z, Wang N (2021) Simpletrack: Understanding and rethinking 3d multi-object tracking. arXiv:abs\/2111.09621"},{"key":"7466_CR6","doi-asserted-by":"publisher","unstructured":"Singh D, Kumar A, Singh R (2023) In: Kumar, A., Jain, R., Vairamani, A.D., Nayyar, A. (eds.) Multiple Object Tracking of Autonomous Vehicles for Sustainable and Smart Cities, pp. 201\u2013219. Springer, Singapore. https:\/\/doi.org\/10.1007\/978-981-99-3288-7_9","DOI":"10.1007\/978-981-99-3288-7_9"},{"key":"7466_CR7","doi-asserted-by":"crossref","unstructured":"Bumanis N, V\u012btols G, Arhipova I, Solmanis E (2021) Multi-object tracking for urban and multilane traffic: Building blocks for real-world application. In: International Conference on Enterprise Information Systems. https:\/\/api.semanticscholar.org\/CorpusID:235259256","DOI":"10.5220\/0010467807290736"},{"key":"7466_CR8","doi-asserted-by":"publisher","unstructured":"Park J, Hong J, Shim W, Jung D-J (2023) Multi-object tracking on swir images for city surveillance in an edge-computing environment. Sensors 23(14) https:\/\/doi.org\/10.3390\/s23146373","DOI":"10.3390\/s23146373"},{"key":"7466_CR9","doi-asserted-by":"publisher","unstructured":"Basar T (2001) A new approach to linear filtering and prediction problems, pp. 167\u2013179. https:\/\/doi.org\/10.1109\/9780470544334.ch9","DOI":"10.1109\/9780470544334.ch9"},{"key":"7466_CR10","doi-asserted-by":"crossref","unstructured":"Kuhn HW (1955) The hungarian method for the assignment problem. Naval Research Logistics (NRL) 52","DOI":"10.1002\/nav.20053"},{"key":"7466_CR11","unstructured":"He L, Liao X, Liu W, Liu X, Cheng P, Mei T (2020) Fastreid: A pytorch toolbox for general instance re-identification. Proceedings of the 31st ACM International Conference on Multimedia"},{"key":"7466_CR12","doi-asserted-by":"crossref","unstructured":"Zhang Y, Sun P, Jiang Y, Yu D, Yuan Z, Luo P, Liu W, Wang X (2021) Bytetrack: Multi-object tracking by associating every detection box. arXiv:abs\/2110.06864","DOI":"10.1007\/978-3-031-20047-2_1"},{"key":"7466_CR13","unstructured":"Gao J, Wang Y, Yap K.-h, Garg K, Han B (2023) Occlutrack: Rethinking awareness of occlusion for enhancing multiple pedestrian tracking. arXiv:abs\/2309.10360"},{"key":"7466_CR14","doi-asserted-by":"crossref","unstructured":"Li J, Ding Y, Wei H.-L (2022) Simpletrack: Rethinking and improving the jde approach for multi-object tracking. Sensors (Basel, Switzerland) 22","DOI":"10.3390\/s22155863"},{"key":"7466_CR15","doi-asserted-by":"publisher","unstructured":"Maggiolino G, Ahmad A, Cao J, Kitani K (2023) Deep oc-sort: Multi-pedestrian tracking by adaptive re-identification. In: 2023 IEEE International Conference on Image Processing (ICIP), pp. 3025\u20133029. https:\/\/doi.org\/10.1109\/ICIP49359.2023.10222576","DOI":"10.1109\/ICIP49359.2023.10222576"},{"key":"7466_CR16","unstructured":"Wang Y, Hsieh J-W, Chen P-Y, Chang M-C (2022). Smiletrack: Similarity learning for multiple object tracking. arXiv:abs\/2211.08824"},{"key":"7466_CR17","doi-asserted-by":"publisher","unstructured":"Xiang Y, Alahi A, Savarese S (2015) Learning to track: Online multi-object tracking by decision making. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 4705\u20134713 . https:\/\/doi.org\/10.1109\/ICCV.2015.534","DOI":"10.1109\/ICCV.2015.534"},{"key":"7466_CR18","doi-asserted-by":"publisher","unstructured":"Stadler D, Beyerer J (2023) An improved association pipeline for multi-person tracking. In: 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 3170\u20133179. https:\/\/doi.org\/10.1109\/CVPRW59228.2023.00319","DOI":"10.1109\/CVPRW59228.2023.00319"},{"issue":"6","key":"7466_CR19","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren S, He K, Girshick R, Sun J (2017) Faster r-cnn: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137\u20131149. https:\/\/doi.org\/10.1109\/TPAMI.2016.2577031","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"7466_CR20","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala SK, Girshick RB, Farhadi A (2015) You only look once: Unified, real-time object detection. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779\u2013788","DOI":"10.1109\/CVPR.2016.91"},{"key":"7466_CR21","doi-asserted-by":"publisher","unstructured":"Redmon J, Farhadi A (2017) Yolo9000: Better, faster, stronger. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6517\u20136525. https:\/\/doi.org\/10.1109\/CVPR.2017.690","DOI":"10.1109\/CVPR.2017.690"},{"key":"7466_CR22","unstructured":"Redmon J, Farhadi A (2018) Yolov3: An incremental improvement. arXiv:abs\/1804.02767"},{"key":"7466_CR23","unstructured":"Bochkovskiy A, Wang C-Y, Liao H-YM (2020) Yolov4: optimal speed and accuracy of object detection. arXiv:abs\/2004.10934"},{"key":"7466_CR24","unstructured":"Zhou X, Wang D, Kr\u201dahenb\u201duhl P (2019) Objects as points. ArXiv abs\/1904.07850"},{"key":"7466_CR25","unstructured":"Ge Z, Liu S, Wang F, Li Z, Sun J (2021) Yolox: Exceeding yolo series in 2021. arXiv:abs\/2107.08430"},{"key":"7466_CR26","doi-asserted-by":"crossref","unstructured":"Zhang Y, Sun P, Jiang Y, Yu D, Yuan Z, Luo P, Liu W, Wang X (2021) Bytetrack: Multi - object tracking by associating every detection box. arXiv:abs\/2110.06864","DOI":"10.1007\/978-3-031-20047-2_1"},{"key":"7466_CR27","unstructured":"Aharon N, Orfaig R, Bobrovsky B.-Z (2022) Bot - sort: Robust associations multi - pedestrian tracking. arXiv:abs\/2206.14651"},{"key":"7466_CR28","doi-asserted-by":"publisher","unstructured":"Chen P-Y, Chang M-C, Hsieh J-W, Chen Y-S (2021) Parallel residual bi - fusion feature pyramid network for accurate single - shot object detection. IEEE Transactions on Image Processing 30, 9099\u20139111 https:\/\/doi.org\/10.1109\/TIP.2021.3118953","DOI":"10.1109\/TIP.2021.3118953"},{"key":"7466_CR29","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1007\/978-3-319-46448-0_45","volume-title":"Computer Vision - ECCV 2016","author":"D Held","year":"2016","unstructured":"Held D, Thrun S, Savarese S (2016) Learning to track at 100 fps with deep regression networks. In: Leibe B, Matas J, Sebe N, Welling M (eds) Computer Vision - ECCV 2016. Springer, Cham, pp 749\u2013765"},{"key":"7466_CR30","unstructured":"Lv W, Zhao Y, Chang Q, Huang K, Wang G, Liu Y (2024) Rt-detrv2: Improved baseline with bag-of-freebies for real-time detection transformer. arXiv:abs\/2407.17140"},{"key":"7466_CR31","unstructured":"Brown RG, Hwang PYC (1996) Introduction to random signals and applied kalman filtering : with matlab exercises and solutions. https:\/\/api.semanticscholar.org\/CorpusID:207982996"},{"key":"7466_CR32","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.neucom.2021.12.104","volume":"476","author":"S Han","year":"2020","unstructured":"Han S, Huang P, Wang H, Yu E, Liu D, Pan X, Zhao J (2020) Mat: Motion - aware multi - object tracking. Neurocomputing 476:75\u201386","journal-title":"Neurocomputing"},{"key":"7466_CR33","doi-asserted-by":"crossref","unstructured":"Bewley A, Ge Z, Ott L, Ramos F.T, Upcroft B (2016) Simple online and realtime tracking. 2016 IEEE International Conference on Image Processing (ICIP), 3464\u20133468","DOI":"10.1109\/ICIP.2016.7533003"},{"key":"7466_CR34","doi-asserted-by":"publisher","unstructured":"Wojke N, Bewley A, Paulus D (2017) Simple online and realtime tracking with a deep association metric. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 3645\u20133649. https:\/\/doi.org\/10.1109\/ICIP.2017.8296962","DOI":"10.1109\/ICIP.2017.8296962"},{"key":"7466_CR35","doi-asserted-by":"publisher","first-page":"3069","DOI":"10.1007\/s11263-021-01513-4","volume":"129","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Wang C, Wang X, Zeng W, Liu W (2020) Fairmot: on the fairness of detection and re - identification in multiple object tracking. Int J Comput Vis 129:3069\u20133087","journal-title":"Int J Comput Vis"},{"key":"7466_CR36","doi-asserted-by":"crossref","unstructured":"Du Y, Wan J.-J, Zhao Y, Zhang B, Tong Z, Dong J (2021) Giaotracker: a comprehensive framework for mcmot with global information and optimizing strategies in visdrone 2021. 2021 IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW), 2809\u20132819","DOI":"10.1109\/ICCVW54120.2021.00315"},{"key":"7466_CR37","doi-asserted-by":"publisher","unstructured":"Du Y, Zhao Z, Song Y, Zhao Y, Su F, Gong T, Meng H (2023) Strongsort: Make deepsort great again. IEEE Trans Multimedia 25, 8725\u20138737 https:\/\/doi.org\/10.1109\/TMM.2023.3240881","DOI":"10.1109\/TMM.2023.3240881"},{"issue":"10","key":"7466_CR38","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6501\/ad5c8b","volume":"35","author":"S Wang","year":"2024","unstructured":"Wang S, Guo Y, Li Y (2024) Robust pedestrian multi-object tracking in the intelligent bus environment. Meas Sci Technol 35(10):105401. https:\/\/doi.org\/10.1088\/1361-6501\/ad5c8b","journal-title":"Meas Sci Technol"},{"key":"7466_CR39","doi-asserted-by":"crossref","unstructured":"Bergmann P, Meinhardt T, Taix\u2019e LL (2019) Tracking without bells and whistles. 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), 941\u2013951","DOI":"10.1109\/ICCV.2019.00103"},{"key":"7466_CR40","doi-asserted-by":"publisher","unstructured":"Stadler D, Beyerer J (2022) Modelling ambiguous assignments for multi - person tracking in crowds. In: 2022 IEEE\/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW), pp. 133\u2013142. https:\/\/doi.org\/10.1109\/WACVW54805.2022.00019","DOI":"10.1109\/WACVW54805.2022.00019"},{"key":"7466_CR41","doi-asserted-by":"publisher","unstructured":"Rublee E, Rabaud V, Konolige K, Bradski G (2011) Orb: an efficient alternative to sift or surf. In: 2011 International Conference on Computer Vision, pp. 2564\u20132571. https:\/\/doi.org\/10.1109\/ICCV.2011.6126544","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"7466_CR42","doi-asserted-by":"crossref","unstructured":"Wang G, Yuan Y, Chen X, Li J, Zhou X (2018) Learning discriminative features with multiple granularities for person re - identification. Proceedings of the 26th ACM international conference on Multimedia","DOI":"10.1145\/3240508.3240552"},{"key":"7466_CR43","doi-asserted-by":"publisher","unstructured":"Stadler D, Beyerer J (2023) An improved association pipeline for multi-person tracking. In: 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 3170\u20133179. https:\/\/doi.org\/10.1109\/CVPRW59228.2023.00319","DOI":"10.1109\/CVPRW59228.2023.00319"},{"issue":"3","key":"7466_CR44","doi-asserted-by":"publisher","first-page":"3123","DOI":"10.1109\/JSEN.2023.3339212","volume":"24","author":"H Mao","year":"2024","unstructured":"Mao H, Chen Y, Li Z, Chen P, Chen F (2024) Sctracker: multi-object tracking with shape and confidence constraints. IEEE Sens J 24(3):3123\u20133130. https:\/\/doi.org\/10.1109\/JSEN.2023.3339212","journal-title":"IEEE Sens J"},{"key":"7466_CR45","doi-asserted-by":"crossref","unstructured":"Zhang H, Wan J, Zhang J, Yuan D, Li X, Yang Y (2024) P2ftrack: Multi-object tracking with motion prior and feature posterior. ACM Trans Multimedia Comput Commun Appl 21(1)","DOI":"10.1145\/3700443"},{"issue":"12","key":"7466_CR46","doi-asserted-by":"publisher","first-page":"9305","DOI":"10.1007\/s11760-024-03547-w","volume":"18","author":"Z Liu","year":"2024","unstructured":"Liu Z, Huang X, Sun J, Zhang X (2024) AMtrack: anti-occlusion multi-object tracking algorithm. Signal, Image Video Proc 18(12):9305\u20139318. https:\/\/doi.org\/10.1007\/s11760-024-03547-w","journal-title":"Signal, Image Video Proc"},{"key":"7466_CR47","doi-asserted-by":"publisher","unstructured":"Miah M, Bilodeau G-A, Saunier N (2025) Learning data association for multi-object tracking using only coordinates. Pattern Recognition 160, 111169 https:\/\/doi.org\/10.1016\/j.patcog.2024.111169","DOI":"10.1016\/j.patcog.2024.111169"},{"key":"7466_CR48","doi-asserted-by":"publisher","unstructured":"Nenavath H, Ashwini K, Jatoth R.K, Mirjalili S (2022) Intelligent trigonometric particle filter for visual tracking. ISA Transactions 128, 460\u2013476 https:\/\/doi.org\/10.1016\/j.isatra.2021.09.014","DOI":"10.1016\/j.isatra.2021.09.014"},{"key":"7466_CR49","doi-asserted-by":"crossref","unstructured":"Meinhardt T, Kirillov A, Taix\u2019e LL, Feichtenhofer C (2021) Trackformer: Multi - object tracking with transformers. 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 8834\u20138844","DOI":"10.1109\/CVPR52688.2022.00864"},{"key":"7466_CR50","unstructured":"Sun P, Jiang Y, Zhang R, Xie E, Cao J, Hu X, Kong T, Yuan Z, Wang C, Luo P (2020) Transtrack: multiple - object tracking with transformer. arXiv:abs\/2012.15460"},{"key":"7466_CR51","unstructured":"Zhu X, Su W, Lu L, Li B, Wang X, Dai J (2020) Deformable detr: Deformable transformers for end - to - end object detection. arXiv:abs\/2010.04159"},{"key":"7466_CR52","doi-asserted-by":"crossref","unstructured":"Xu Y, Ban Y, Delorme G, Gan C, Rus D, Pineda X.A (2021) Transcenter: Transformers with dense representations for multiple - object tracking. IEEE Trans Pattern Anal Mach Intell 45, 7820\u20137835","DOI":"10.1109\/TPAMI.2022.3225078"},{"key":"7466_CR53","doi-asserted-by":"crossref","unstructured":"Zhou K, Yang Y, Cavallaro A, Xiang T (2019) Omni - scale feature learning for person re - identification. 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), 3701\u20133711","DOI":"10.1109\/ICCV.2019.00380"},{"key":"7466_CR54","doi-asserted-by":"crossref","unstructured":"Xu Y, Osep A, Ban Y, Horaud R, Taix\u2019e L.L, Pineda X.A (2019) How to train your deep multi - object tracker. 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 6786\u20136795","DOI":"10.1109\/CVPR42600.2020.00682"},{"key":"7466_CR55","doi-asserted-by":"crossref","unstructured":"Lu Z, Rathod V, Votel R, Huang J (2020) Retinatrack: Online single stage joint detection and tracking. 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 14656\u201314666","DOI":"10.1109\/CVPR42600.2020.01468"},{"key":"7466_CR56","doi-asserted-by":"crossref","unstructured":"Peng J, Wang C, Wan F, Wu Y, Wang Y, Tai Y, Wang C, Li J, Huang F, Fu Y (2020) Chained - tracker: Chaining paired attentive regression results for end - to - end joint multiple - object detection and tracking. arXiv: abs\/2007.14557","DOI":"10.1007\/978-3-030-58548-8_9"},{"key":"7466_CR57","doi-asserted-by":"publisher","unstructured":"Yu E, Li Z, Han S, Wang H (2023) Relationtrack: Relation - aware multiple object tracking with decoupled representation. IEEE Trans Multimedia 25, 2686\u20132697 https:\/\/doi.org\/10.1109\/TMM.2022.3150169","DOI":"10.1109\/TMM.2022.3150169"},{"key":"7466_CR58","doi-asserted-by":"publisher","unstructured":"Wang Q, Zheng Y, Pan P, Xu Y (2021) Multiple object tracking with correlation learning. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3875\u20133885. https:\/\/doi.org\/10.1109\/CVPR46437.2021.00387","DOI":"10.1109\/CVPR46437.2021.00387"},{"key":"7466_CR59","doi-asserted-by":"publisher","unstructured":"Liang C, Zhang Z, Zhou X, Li B, Zhu S, Hu W (2022) Rethinking the competition between detection and reid in multiobject tracking. IEEE Transactions on Image Processing 31, 3182\u20133196 https:\/\/doi.org\/10.1109\/TIP.2022.3165376","DOI":"10.1109\/TIP.2022.3165376"},{"key":"7466_CR60","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J, Houlsby N (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv:abs\/2010.11929"},{"key":"7466_CR61","unstructured":"Aharon N, Orfaig R, Bobrovsky B-Z (2022) Bot-sort: Robust associations multi-pedestrian tracking. arXiv:abs\/2206.14651"},{"key":"7466_CR62","unstructured":"Vaswani A, Shazeer N.M, Parmar N, Uszkoreit J, Jones L, Gomez A.N, Kaiser L, Polosukhin I (2017) Attention is all you need. In: Neural Information Processing Systems. https:\/\/api.semanticscholar.org\/CorpusID:13756489"},{"key":"7466_CR63","doi-asserted-by":"publisher","unstructured":"Wang C-Y, Mark\u00a0Liao H-Y, Wu Y-H, Chen P-Y, Hsieh J-W, Yeh I-H (2020) Cspnet: A new backbone that can enhance learning capability of cnn. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1571\u20131580. https:\/\/doi.org\/10.1109\/CVPRW50498.2020.00203","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"7466_CR64","unstructured":"Milan A, Taix\u2019e LL, Reid ID, Roth S, Schindler K (2016) Mot16: a benchmark for multi - object tracking. arXiv:abs\/1603.00831"},{"key":"7466_CR65","unstructured":"Dendorfer P, Rezatofighi H, Milan A, Shi JQ, Cremers D, Reid ID, Roth S, Schindler K, Taix\u2019e LL (2020) Mot20: a benchmark for multi object tracking in crowded scenes. arXiv:abs\/2003.09003"},{"key":"7466_CR66","doi-asserted-by":"publisher","unstructured":"Sun P, Cao J, Jiang Y, Yuan Z, Bai S, Kitani K, Luo P (2022) Dancetrack: multi - object tracking in uniform appearance and diverse motion. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 20961\u201320970. https:\/\/doi.org\/10.1109\/CVPR52688.2022.02032","DOI":"10.1109\/CVPR52688.2022.02032"},{"key":"7466_CR67","doi-asserted-by":"publisher","unstructured":"Sch\u00f6ps T, Sch\u00f6nberger J.L, Galliani S, Sattler T, Schindler K, Pollefeys M, Geiger A (2017) A multi - view stereo benchmark with high - resolution images and multi - camera videos. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2538\u20132547. https:\/\/doi.org\/10.1109\/CVPR.2017.272","DOI":"10.1109\/CVPR.2017.272"},{"key":"7466_CR68","doi-asserted-by":"publisher","unstructured":"Dollar P, Wojek C, Schiele B, Perona P (2009) Pedestrian detection: a benchmark. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 304\u2013311. https:\/\/doi.org\/10.1109\/CVPR.2009.5206631","DOI":"10.1109\/CVPR.2009.5206631"},{"key":"7466_CR69","doi-asserted-by":"crossref","unstructured":"Zhang S, Benenson R, Schiele B (2017) Citypersons: A diverse dataset for pedestrian detection. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 4457\u20134465","DOI":"10.1109\/CVPR.2017.474"},{"key":"7466_CR70","unstructured":"Shao S, Zhao Z, Li B, Xiao T, Yu G, Zhang X, Sun J (2018) Crowdhuman: a benchmark for detecting human in a crowd. arXiv:abs\/1805.00123"},{"key":"7466_CR71","doi-asserted-by":"publisher","unstructured":"Ess A, Leibe B, Schindler K, Van\u00a0Gool L (2008) A mobile vision system for robust multi - person tracking. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20138. https:\/\/doi.org\/10.1109\/CVPR.2008.4587581","DOI":"10.1109\/CVPR.2008.4587581"},{"key":"7466_CR72","doi-asserted-by":"crossref","unstructured":"Xiao T, Li S, Wang B, Lin L, Wang X (2016) Joint detection and identification feature learning for person search. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3376\u20133385","DOI":"10.1109\/CVPR.2017.360"},{"key":"7466_CR73","doi-asserted-by":"publisher","unstructured":"Zheng L, Zhang H, Sun S, Chandraker M, Yang Y, Tian Q (2017) Person re - identification in the wild. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3346\u20133355. https:\/\/doi.org\/10.1109\/CVPR.2017.357","DOI":"10.1109\/CVPR.2017.357"},{"key":"7466_CR74","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2008\/246309","volume":"2008","author":"K Bernardin","year":"2008","unstructured":"Bernardin K, Stiefelhagen R (2008) Evaluating multiple object tracking performance: the clear mot metrics. EURASIP J Image Video Proc 2008:1\u201310","journal-title":"EURASIP J Image Video Proc"},{"key":"7466_CR75","doi-asserted-by":"crossref","unstructured":"Ristani E, Solera F, Zou R.S, Cucchiara R, Tomasi C (2016) Performance measures and a data set for multi - target, multi - camera tracking. In: ECCV Workshops. https:\/\/api.semanticscholar.org\/CorpusID:5584770","DOI":"10.1007\/978-3-319-48881-3_2"},{"key":"7466_CR76","doi-asserted-by":"crossref","unstructured":"Luiten J, Osep A, Dendorfer P, Torr PHS, Geiger A, Taix\u2019e LL, Leibe B (2020) Hota: a higher order metric for evaluating multi - object tracking. Int J Comput Vis 129, 548\u2013578","DOI":"10.1007\/s11263-020-01375-2"},{"key":"7466_CR77","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Maire M, Belongie S.J, Hays J, Perona P, Ramanan D, Doll\u2019ar P, Zitnick CL (2014) Microsoft coco: Common objects in context. In: European Conference on Computer Vision. https:\/\/api.semanticscholar.org\/CorpusID:14113767","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"7466_CR78","doi-asserted-by":"crossref","unstructured":"Pang B, Li Y, Zhang Y, Li M, Lu C (2020) Tubetk: adopting tubes to track multi-object in a one-step training model. 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 6307\u20136317","DOI":"10.1109\/CVPR42600.2020.00634"},{"key":"7466_CR79","unstructured":"Zeng F, Dong B, Wang T, Chen C, Zhang X, Wei Y (2021) Motr: End-to-end multiple-object tracking with transformer. arXiv:abs\/2105.03247"},{"key":"7466_CR80","doi-asserted-by":"crossref","unstructured":"Peng J, Wang C, Wan F, Wu Y, Wang Y, Tai Y, Wang C, Li J, Huang F, Fu Y (2020) Chained-tracker: chaining paired attentive regression results for end-to-end joint multiple-object detection and tracking. arXiv:abs\/2007.14557","DOI":"10.1007\/978-3-030-58548-8_9"},{"key":"7466_CR81","doi-asserted-by":"crossref","unstructured":"Zhou X, Koltun V, Kr\u00e4henb\u00fchl P (2020) Tracking objects as points. arXiv:abs\/2004.01177","DOI":"10.1007\/978-3-030-58548-8_28"},{"key":"7466_CR82","doi-asserted-by":"crossref","unstructured":"Pang J, Qiu L, Li X, Chen H, Li Q, Darrell T, Yu F (2020) Quasi-dense similarity learning for multiple object tracking. 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 164\u2013173","DOI":"10.1109\/CVPR46437.2021.00023"},{"key":"7466_CR83","doi-asserted-by":"publisher","unstructured":"Wu J, Cao J, Song L, Wang Y, Yang M, Yuan J (2021) Track to detect and segment: an online multi-object tracker. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12347\u201312356. https:\/\/doi.org\/10.1109\/CVPR46437.2021.01217","DOI":"10.1109\/CVPR46437.2021.01217"},{"key":"7466_CR84","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.neucom.2021.12.104","volume":"476","author":"S Han","year":"2020","unstructured":"Han S, Huang P, Wang H, Yu E, Liu D, Pan X, Zhao J (2020) Mat: Motion-aware multi-object tracking. Neurocomputing 476:75\u201386","journal-title":"Neurocomputing"},{"key":"7466_CR85","doi-asserted-by":"publisher","unstructured":"Zheng L, Tang M, Chen Y, Zhu G, Wang J, Lu H (2021) Improving multiple object tracking with single object tracking. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2453\u20132462. https:\/\/doi.org\/10.1109\/CVPR46437.2021.00248","DOI":"10.1109\/CVPR46437.2021.00248"},{"key":"7466_CR86","doi-asserted-by":"publisher","unstructured":"Wang Y, Kitani K, Weng X (2021) Joint object detection and multi-object tracking with graph neural networks. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 13708\u201313715. https:\/\/doi.org\/10.1109\/ICRA48506.2021.9561110","DOI":"10.1109\/ICRA48506.2021.9561110"},{"key":"7466_CR87","doi-asserted-by":"crossref","unstructured":"Tokmakov P, Li J, Burgard W, Gaidon A (2021) Learning to track with object permanence. 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), 10840\u201310849","DOI":"10.1109\/ICCV48922.2021.01068"},{"key":"7466_CR88","doi-asserted-by":"publisher","first-page":"3182","DOI":"10.1109\/TIP.2022.3165376","volume":"31","author":"C Liang","year":"2020","unstructured":"Liang C, Zhang Z, Lu Y, Zhou X, Li B, Ye X, Zou J (2020) Rethinking the competition between detection and reid in multiobject tracking. IEEE Trans Image Proc 31:3182\u20133196","journal-title":"IEEE Trans Image Proc"},{"key":"7466_CR89","doi-asserted-by":"crossref","unstructured":"Shuai B, Berneshawi A.G, Li X, Modolo D, Tighe J (2021) Siammot: Siamese multi-object tracking. 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 12367\u201312377","DOI":"10.1109\/CVPR46437.2021.01219"},{"key":"7466_CR90","doi-asserted-by":"publisher","unstructured":"Wang Q, Zheng Y, Pan P, Xu Y (2021) Multiple object tracking with correlation learning. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3875\u20133885. https:\/\/doi.org\/10.1109\/CVPR46437.2021.00387","DOI":"10.1109\/CVPR46437.2021.00387"},{"key":"7466_CR91","doi-asserted-by":"crossref","unstructured":"Chu P, Wang J, You Q, Ling H, Liu Z (2021). Transmot: spatial-temporal graph transformer for multiple object tracking. 2023 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), 4859\u20134869","DOI":"10.1109\/WACV56688.2023.00485"},{"key":"7466_CR92","doi-asserted-by":"publisher","unstructured":"Yang F, Chang X, Sakti S, Wu Y, Nakamura S (2021) Remot: a model-agnostic refinement for multiple object tracking. Image Vis Comput 106, 104091. https:\/\/doi.org\/10.1016\/j.imavis.2020.104091","DOI":"10.1016\/j.imavis.2020.104091"},{"key":"7466_CR93","doi-asserted-by":"crossref","unstructured":"Cao J, Weng X, Khirodkar R, Pang J, Kitani K (2022) Observation-centric sort: rethinking sort for robust multi-object tracking. 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 9686\u20139696","DOI":"10.1109\/CVPR52729.2023.00934"},{"key":"7466_CR94","doi-asserted-by":"publisher","unstructured":"Stadler D, Beyerer J (2022) Modelling ambiguous assignments for multi-person tracking in crowds. In: 2022 IEEE\/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW), pp. 133\u2013142. https:\/\/doi.org\/10.1109\/WACVW54805.2022.00019","DOI":"10.1109\/WACVW54805.2022.00019"},{"key":"7466_CR95","unstructured":"Liu Z, Wang X, Wang C, Liu W, Bai X (2023) Sparsetrack: Multi-object tracking by performing scene decomposition based on pseudo-depth. arXiv:abs\/2306.05238"},{"key":"7466_CR96","doi-asserted-by":"crossref","unstructured":"Zhou X, Yin T, Koltun V, Kr\u00e4henb\u00fchl P (2022) Global tracking transformers. 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 8761\u20138770","DOI":"10.1109\/CVPR52688.2022.00857"},{"key":"7466_CR97","unstructured":"Girbau A, Marqu\u2019es F, Satoh S (2022) Multiple object tracking from appearance by hierarchically clustering tracklets. In: British Machine Vision Conference. https:\/\/api.semanticscholar.org\/CorpusID:252762147"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07466-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-025-07466-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07466-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T14:21:21Z","timestamp":1751898081000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-025-07466-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,7]]},"references-count":97,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["7466"],"URL":"https:\/\/doi.org\/10.1007\/s11227-025-07466-6","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,7]]},"assertion":[{"value":"16 May 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 July 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"1115"}}