{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T15:43:34Z","timestamp":1786981414221,"version":"build-2736575974"},"reference-count":61,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2023,7,27]],"date-time":"2023-07-27T00:00:00Z","timestamp":1690416000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,27]],"date-time":"2023-07-27T00:00:00Z","timestamp":1690416000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61773330"],"award-info":[{"award-number":["61773330"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-023-16027-z","type":"journal-article","created":{"date-parts":[[2023,7,27]],"date-time":"2023-07-27T06:02:21Z","timestamp":1690437741000},"page":"19465-19484","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Multi-object tracking using context-sensitive enhancement via feature fusion"],"prefix":"10.1007","volume":"83","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2372-4947","authenticated-orcid":false,"given":"Yan","family":"Zhou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junyu","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongli","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaolin","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,7,27]]},"reference":[{"key":"16027_CR1","doi-asserted-by":"crossref","unstructured":"Adame BO, Salau AO, Subbanna BC, Tirupal T, Sultana SF (2020) Multimodal medical image fusion based on intuitionistic fuzzy sets. In: 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE), IEEE, pp 131\u2013134","DOI":"10.1109\/WIECON-ECE52138.2020.9397963"},{"key":"16027_CR2","unstructured":"Aharon N, Orfaig R, Bobrovsky BZ (2022) Bot-sort: Robust associations multi-pedestrian tracking. arXiv preprint arXiv:2206.14651"},{"issue":"19","key":"16027_CR3","doi-asserted-by":"publisher","first-page":"25199","DOI":"10.1007\/s11042-018-5781-3","volume":"77","author":"T Badal","year":"2018","unstructured":"Badal T, Nain N, Ahmed M (2018) Online multi-object tracking: multiple instance based target appearance model. Multimedia Tools and Applications 77(19):25199\u201325221","journal-title":"Multimedia Tools and Applications"},{"key":"16027_CR4","doi-asserted-by":"crossref","unstructured":"Bergmann P, Meinhardt T, Leal-Taixe L (2019) Tracking without bells and whistles. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 941\u2013951","DOI":"10.1109\/ICCV.2019.00103"},{"key":"16027_CR5","doi-asserted-by":"crossref","unstructured":"Bewley A, Ge Z, Ott L, Ramos F, Upcroft B (2016) Simple online and realtime tracking. In: 2016 IEEE international conference on image processing (ICIP), IEEE, pp 3464\u20133468","DOI":"10.1109\/ICIP.2016.7533003"},{"key":"16027_CR6","doi-asserted-by":"crossref","unstructured":"Bochinski E, Eiselein V, Sikora T (2017) High-speed tracking-by-detection without using image information. In: 2017 14th IEEE international conference on advanced video and signal based surveillance (AVSS), IEEE, pp 1\u20136","DOI":"10.1109\/AVSS.2017.8078516"},{"issue":"104","key":"16027_CR7","first-page":"468","volume":"123","author":"T Bouraffa","year":"2022","unstructured":"Bouraffa T, Feng Z, Yan L, Xia Y, Xiao B (2022) Multi-feature fusion tracking algorithm based on peak-context learning. Image Vis Comput 123(104):468","journal-title":"Image Vis Comput"},{"key":"16027_CR8","doi-asserted-by":"crossref","unstructured":"Bras\u00f3 G, Leal-Taix\u00e9 L (2020) Learning a neural solver for multiple object tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 6247\u20136257","DOI":"10.1109\/CVPR42600.2020.00628"},{"issue":"47","key":"16027_CR9","doi-asserted-by":"publisher","first-page":"35333","DOI":"10.1007\/s11042-019-07747-2","volume":"79","author":"L Chen","year":"2020","unstructured":"Chen L, Lou J, Xu F, Ren M (2020) Grid-based multi-object tracking with siamese cnn based appearance edge and access region mechanism. Multimedia Tools and Applications 79(47):35333\u201335351","journal-title":"Multimedia Tools and Applications"},{"key":"16027_CR10","doi-asserted-by":"crossref","unstructured":"Chu P, Wang J, You Q, Ling H, Liu Z (2023) Transmot: Spatial-temporal graph transformer for multiple object tracking. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp 4870\u20134880","DOI":"10.1109\/WACV56688.2023.00485"},{"key":"16027_CR11","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1016\/j.ins.2021.06.084","volume":"577","author":"D Elayaperumal","year":"2021","unstructured":"Elayaperumal D, Joo YH (2021) Robust visual object tracking using context-based spatial variation via multi-feature fusion. Inf Sci 577:467\u2013482","journal-title":"Inf Sci"},{"key":"16027_CR12","doi-asserted-by":"crossref","unstructured":"Fang K, Xiang Y, Li X, Savarese S (2018) Recurrent autoregressive networks for online multi-object tracking. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE, pp 466\u2013475","DOI":"10.1109\/WACV.2018.00057"},{"key":"16027_CR13","unstructured":"Faster R (2015) Towards real-time object detection with region proposal networks. Advances in neural information processing systems 9199(10.5555):2969239\u20132969250"},{"issue":"9","key":"16027_CR14","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","volume":"32","author":"PF Felzenszwalb","year":"2010","unstructured":"Felzenszwalb PF, Girshick RB, McAllester D, Ramanan D (2010) Object detection with discriminatively trained part-based models. IEEE Trans Pattern Anal Mach Intell 32(9):1627\u20131645","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"43","key":"16027_CR15","doi-asserted-by":"publisher","first-page":"32623","DOI":"10.1007\/s11042-020-09546-6","volume":"79","author":"Lh Fu","year":"2020","unstructured":"Fu Lh, Ding Y, Du YB, Zhang B, Wang LY, Wang D (2020) Siammn: Siamese modulation network for visual object tracking. Multimedia Tools and Applications 79(43):32623\u201332641","journal-title":"Multimedia Tools and Applications"},{"issue":"4","key":"16027_CR16","doi-asserted-by":"publisher","first-page":"965","DOI":"10.1007\/s11760-021-02041-x","volume":"16","author":"X Gao","year":"2022","unstructured":"Gao X, Shen Z, Yang Y (2022) Multi-object tracking with siamese-rpn and adaptive matching strategy. SIViP 16(4):965\u2013973","journal-title":"SIViP"},{"key":"16027_CR17","doi-asserted-by":"crossref","unstructured":"Guo S, Wang J, Wang X, Tao D (2021) Online multiple object tracking with cross-task synergy. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 8136\u20138145","DOI":"10.1109\/CVPR46437.2021.00804"},{"key":"16027_CR18","unstructured":"Hornakova A, Henschel R, Rosenhahn B, Swoboda P (2020) Lifted disjoint paths with application in multiple object tracking. In: International conference on machine learning, PMLR, pp 4364\u20134375"},{"key":"16027_CR19","doi-asserted-by":"crossref","unstructured":"Jain S, Salau AO (2021) Multimodal image fusion employing discrete cosine transform. In: 2021 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE), IEEE, pp 5\u20138","DOI":"10.1109\/WIECON-ECE54711.2021.9829686"},{"key":"16027_CR20","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 (2019) Multiple object tracking with attention to appearance, structure, motion and size. IEEE Access 7:104423\u2013104434","journal-title":"IEEE Access"},{"key":"16027_CR21","doi-asserted-by":"crossref","unstructured":"Kim C, Li F, Ciptadi A, Rehg JM (2015) Multiple hypothesis tracking revisited. In: Proceedings of the IEEE international conference on computer vision, pp 4696\u20134704","DOI":"10.1109\/ICCV.2015.533"},{"key":"16027_CR22","doi-asserted-by":"crossref","unstructured":"Kim C, Li F, Rehg JM (2018) Multi-object tracking with neural gating using bilinear lstm. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 200\u2013215","DOI":"10.1007\/978-3-030-01237-3_13"},{"key":"16027_CR23","doi-asserted-by":"crossref","unstructured":"Kim C, Fuxin L, Alotaibi M, Rehg JM (2021) Discriminative appearance modeling with multi-track pooling for real-time multi-object tracking. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 9553\u20139562","DOI":"10.1109\/CVPR46437.2021.00943"},{"key":"16027_CR24","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1016\/j.patcog.2019.02.004","volume":"90","author":"DY Kim","year":"2019","unstructured":"Kim DY, Vo BN, Vo BT, Jeon M (2019) A labeled random finite set online multi-object tracker for video data. Pattern Recogn 90:377\u2013389","journal-title":"Pattern Recogn"},{"key":"16027_CR25","doi-asserted-by":"crossref","unstructured":"Li J, Gao X, Jiang T (2020) Graph networks for multiple object tracking. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp 719\u2013728","DOI":"10.1109\/WACV45572.2020.9093347"},{"key":"16027_CR26","doi-asserted-by":"crossref","unstructured":"Lin TY, Doll\u00e1r P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2117\u20132125","DOI":"10.1109\/CVPR.2017.106"},{"key":"16027_CR27","doi-asserted-by":"crossref","unstructured":"Liu J, Li C, Liang F, Lin C, Sun M, Yan J, Ouyang W, Xu D (2021) Inception convolution with efficient dilation search. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 11486\u201311495","DOI":"10.1109\/CVPR46437.2021.01132"},{"key":"16027_CR28","doi-asserted-by":"crossref","unstructured":"Liu S, Qi L, Qin H, Shi J, Jia J (2018) Path aggregation network for instance segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8759\u20138768","DOI":"10.1109\/CVPR.2018.00913"},{"key":"16027_CR29","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) Ssd: Single shot multibox detector. In: European conference on computer vision, Springer, pp 21\u201337","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"16027_CR30","doi-asserted-by":"crossref","unstructured":"Lu Z, Rathod V, Votel R, Huang J (2020) Retinatrack: Online single stage joint detection and tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 14668\u201314678","DOI":"10.1109\/CVPR42600.2020.01468"},{"issue":"6","key":"16027_CR31","doi-asserted-by":"publisher","first-page":"7077","DOI":"10.1007\/s11042-018-6467-6","volume":"78","author":"N Mahmoudi","year":"2019","unstructured":"Mahmoudi N, Ahadi SM, Rahmati M (2019) Multi-target tracking using cnn-based features: Cnnmtt. Multimedia Tools and Applications 78(6):7077\u20137096","journal-title":"Multimedia Tools and Applications"},{"key":"16027_CR32","doi-asserted-by":"crossref","unstructured":"Pang B, Li Y, Zhang Y, Li M, Lu C (2020a) Tubetk: Adopting tubes to track multi-object in a one-step training model. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 6308\u20136318","DOI":"10.1109\/CVPR42600.2020.00634"},{"key":"16027_CR33","doi-asserted-by":"crossref","unstructured":"Pang Y, Li F, Qiao X, Gilman A (2020b) Real-time tracking based on deep feature fusion. Multimedia Tools and Applications 79(37):27229\u201327255","DOI":"10.1007\/s11042-020-09267-w"},{"key":"16027_CR34","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. In: European conference on computer vision, Springer, pp 145\u2013161","DOI":"10.1007\/978-3-030-58548-8_9"},{"key":"16027_CR35","doi-asserted-by":"crossref","unstructured":"Qin W, Du H, Zhang X Ma Z, Ren X, Luo T (2021) Joint prediction and association for deep feature multiple object tracking. In: Journal of Physics: Conference Series, IOP Publishing, p 012021","DOI":"10.1088\/1742-6596\/2026\/1\/012021"},{"key":"16027_CR36","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 28"},{"issue":"3","key":"16027_CR37","doi-asserted-by":"publisher","first-page":"1515","DOI":"10.11591\/ijeecs.v24.i3.pp1515-1522","volume":"24","author":"AO Salau","year":"2021","unstructured":"Salau AO, Jain S, Eneh JN (2021) A review of various image fusion types and transform. Indonesian Journal of Electrical Engineering and Computer Science 24(3):1515\u20131522","journal-title":"Indonesian Journal of Electrical Engineering and Computer Science"},{"key":"16027_CR38","doi-asserted-by":"crossref","unstructured":"Sanchez-Matilla R, Poiesi F, Cavallaro A (2016) Online multi-target tracking with strong and weak detections. In: European Conference on Computer Vision, Springer, pp 84\u201399","DOI":"10.1007\/978-3-319-48881-3_7"},{"key":"16027_CR39","doi-asserted-by":"crossref","unstructured":"Shuai B, Berneshawi A, Li X, Modolo D, Tighe J (2021) Siammot: Siamese multi-object tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 12372\u201312382","DOI":"10.1109\/CVPR46437.2021.01219"},{"key":"16027_CR40","doi-asserted-by":"crossref","unstructured":"Song Ym, Jeon M (2016) Online multiple object tracking with the hierarchically adopted gm-phd filter using motion and appearance. In: 2016 IEEE International conference on consumer electronics-Asia (ICCE-Asia), IEEE, pp 1\u20134","DOI":"10.1109\/ICCE-Asia.2016.7804800"},{"issue":"1","key":"16027_CR41","first-page":"104","volume":"43","author":"S Sun","year":"2019","unstructured":"Sun S, Akhtar N, Song H, Mian A, Shah M (2019) Deep affinity network for multiple object tracking. IEEE Trans Pattern Anal Mach Intell 43(1):104\u2013119","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"16027_CR42","doi-asserted-by":"crossref","unstructured":"Takala V, Pietikainen M (2007) Multi-object tracking using color, texture and motion. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, pp 1\u20137","DOI":"10.1109\/CVPR.2007.383506"},{"key":"16027_CR43","doi-asserted-by":"crossref","unstructured":"Tan M, Pang R, Le QV (2020) Efficientdet: Scalable and efficient object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 10781\u201310790","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"16027_CR44","doi-asserted-by":"crossref","unstructured":"Tang X, Du DK, He Z, Liu J (2018) Pyramidbox: A context-assisted single shot face detector. In: Proceedings of the European conference on computer vision (ECCV), pp 797\u2013813","DOI":"10.1007\/978-3-030-01240-3_49"},{"key":"16027_CR45","doi-asserted-by":"crossref","unstructured":"Tokmakov P, Li J, Burgard W, Gaidon A (2021) Learning to track with object permanence. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 10,860\u201310,869","DOI":"10.1109\/ICCV48922.2021.01068"},{"key":"16027_CR46","unstructured":"Wan J, Zhang H, Zhang J, Ding Y, Yang Y, Li Y, Li X (2022) Dsrrtracker: Dynamic search region refinement for attention-based siamese multi-object tracking. arXiv preprint arXiv:2203.10729"},{"key":"16027_CR47","doi-asserted-by":"crossref","unstructured":"Wang L, Xu L, Kim MY, et\u00a0al (2017) Online multiple object tracking via flow and convolutional features. In: 2017 IEEE International Conference on Image Processing (ICIP), IEEE, pp 3630\u20133634","DOI":"10.1109\/ICIP.2017.8296959"},{"key":"16027_CR48","doi-asserted-by":"crossref","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), IEEE, pp 13,708\u201313,715","DOI":"10.1109\/ICRA48506.2021.9561110"},{"key":"16027_CR49","doi-asserted-by":"crossref","unstructured":"Wang Z, Zheng L, Liu Y, et\u00a0al (2020) Towards real-time multi-object tracking. In: European Conference on Computer Vision, Springer, pp 107\u2013122","DOI":"10.1007\/978-3-030-58621-8_7"},{"key":"16027_CR50","doi-asserted-by":"crossref","unstructured":"Wojke N, Bewley A, Paulus D (2017) Simple online and real-time tracking with a deep association metric. In: 2017 IEEE international conference on image processing (ICIP), IEEE, pp 3645\u20133649","DOI":"10.1109\/ICIP.2017.8296962"},{"key":"16027_CR51","doi-asserted-by":"crossref","unstructured":"Xing D, Evangeliou N, Tsoukalas A, Tzes A (2022) Siamese transformer pyramid networks for real-time uav tracking. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp 2139\u20132148","DOI":"10.1109\/WACV51458.2022.00196"},{"key":"16027_CR52","doi-asserted-by":"crossref","unstructured":"Xu J, Cao Y, Zhang Z, Hu H (2019) Spatial-temporal relation networks for multi-object tracking. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 3988\u20133998","DOI":"10.1109\/ICCV.2019.00409"},{"key":"16027_CR53","doi-asserted-by":"crossref","unstructured":"Yang F, Choi W, Lin Y (2016) Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2129\u20132137","DOI":"10.1109\/CVPR.2016.234"},{"key":"16027_CR54","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.cviu.2016.05.003","volume":"153","author":"M Yang","year":"2016","unstructured":"Yang M, Jia Y (2016) Temporal dynamic appearance modeling for online multi-person tracking. Comput Vis Image Underst 153:16\u201328","journal-title":"Comput Vis Image Underst"},{"key":"16027_CR55","doi-asserted-by":"crossref","unstructured":"Yu F, Li W, Li Q, Liu Y, Shi X, Yan J (2016) Poi: Multiple object tracking with high performance detection and appearance feature. In: European Conference on Computer Vision, Springer, pp 36\u201342","DOI":"10.1007\/978-3-319-48881-3_3"},{"key":"16027_CR56","doi-asserted-by":"crossref","unstructured":"Zeng F, Dong B, Wang T, Chen C, Zhang X, Wei Y. Motr: End-to-end multiple-object tracking with transformer. arxiv 2021. arXiv preprint arXiv:2105.03247","DOI":"10.1007\/978-3-031-19812-0_38"},{"issue":"7","key":"16027_CR57","doi-asserted-by":"publisher","first-page":"3609","DOI":"10.3390\/s23073609","volume":"23","author":"T Zhang","year":"2023","unstructured":"Zhang T, Sun R, Wan Y et al (2023) Msffal: Few-shot object detection via multi-scale feature fusion and attentive learning. Sensors 23(7):3609","journal-title":"Sensors"},{"key":"16027_CR58","doi-asserted-by":"crossref","unstructured":"Zhang Y, Sun P, Jiang Y, Yu D, Weng F, Yuan Z, Luo P, Liu W, Wang X (2022) Bytetrack: Multi-object tracking by associating every detection box. In: Computer Vision\u2013ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23\u201327, 2022, Proceedings, Part XXII, Springer, pp 1\u201321","DOI":"10.1007\/978-3-031-20047-2_1"},{"key":"16027_CR59","doi-asserted-by":"crossref","unstructured":"Zhou X, Koltun V, Kr\u00e4henb\u00fchl P (2020) Tracking objects as points. In: European Conference on Computer Vision, Springer, pp 474\u2013490","DOI":"10.1007\/978-3-030-58548-8_28"},{"key":"16027_CR60","doi-asserted-by":"crossref","unstructured":"Zhou Z, Xing J, Zhang M, Hu W (2018) Online multi-target tracking with tensor-based high-order graph matching. In: 2018 24th International Conference on Pattern Recognition (ICPR), IEEE, pp 1809\u20131814","DOI":"10.1109\/ICPR.2018.8545450"},{"key":"16027_CR61","doi-asserted-by":"crossref","unstructured":"Zou Z, Huang J, Luo P (2022) Compensation tracker: reprocessing lost object for multi-object tracking. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp 307\u2013317","DOI":"10.1109\/WACV51458.2022.00273"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16027-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-16027-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16027-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,18]],"date-time":"2024-02-18T18:32:30Z","timestamp":1708281150000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-16027-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,27]]},"references-count":61,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["16027"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-16027-z","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,27]]},"assertion":[{"value":"9 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 May 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 June 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 July 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors state that they have no conflicting financial interests or personal connections that may have influenced the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}