{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T18:30:34Z","timestamp":1780425034409,"version":"3.54.1"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,6,4]],"date-time":"2023-06-04T00:00:00Z","timestamp":1685836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,4]],"date-time":"2023-06-04T00:00:00Z","timestamp":1685836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"cooperation project of Anhui Institute of Future Technologies with enterprises","award":["No. 18"],"award-info":[{"award-number":["No. 18"]}]},{"name":"practice and innovation project of Anhui Polytechnic University for postgraduate","award":["No. 2"],"award-info":[{"award-number":["No. 2"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s00371-023-02901-2","type":"journal-article","created":{"date-parts":[[2023,6,4]],"date-time":"2023-06-04T05:01:29Z","timestamp":1685854889000},"page":"2049-2065","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A lightweight scheme of deep appearance extraction for robust online multi-object tracking"],"prefix":"10.1007","volume":"40","author":[{"given":"Yi","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9836-0869","authenticated-orcid":false,"given":"Youyu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuanen","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dezhang","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanbao","family":"Tao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,6,4]]},"reference":[{"key":"2901_CR1","doi-asserted-by":"crossref","unstructured":"Kishi, N., Shinkuma, R., Oka, M., et al.: Multi-object tracking for road surveillance without using features of image data. In: 2021 IEEE Global Communications Conference (GLOBECOM). pp. 1\u20136 (2021)","DOI":"10.1109\/GLOBECOM46510.2021.9686010"},{"issue":"7","key":"2901_CR2","doi-asserted-by":"publisher","first-page":"1501","DOI":"10.1007\/s00371-019-01757-9","volume":"36","author":"M Vidanpathirana","year":"2020","unstructured":"Vidanpathirana, M., Sudasingha, I., Vidanapathirana, J., et al.: Tracking and frame-rate enhancement for real-time 2D human pose estimation. Vis. Comput. 36(7), 1501\u20131519 (2020)","journal-title":"Vis. Comput."},{"issue":"20","key":"2901_CR3","doi-asserted-by":"publisher","first-page":"2479","DOI":"10.3390\/electronics10202479","volume":"10","author":"J Chen","year":"2021","unstructured":"Chen, J., Wang, F., Li, C., et al.: Online multiple object tracking using a novel discriminative module for autonomous driving. Electronics 10(20), 2479 (2021). https:\/\/doi.org\/10.3390\/electronics10202479","journal-title":"Electronics"},{"issue":"11","key":"2901_CR4","doi-asserted-by":"publisher","first-page":"3053","DOI":"10.1007\/s13042-020-01220-5","volume":"12","author":"I Ahmed","year":"2021","unstructured":"Ahmed, I., Ahmad, M., Ahmad, A., et al.: Top view multiple people tracking by detection using deep SORT and YOLOv3 with transfer learning: within 5G infrastructure. Int. J. Mach. Learn. Cybern. 12(11), 3053\u20133067 (2021)","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"2901_CR5","unstructured":"Ge, Z., Liu, S., Wang, F., et al.: YOLOX: exceeding YOLO Series in 2021. (2021) [Online]. https:\/\/ui.adsabs.harvard.edu\/abs\/2021arXiv210708430G"},{"key":"2901_CR6","unstructured":"Zhou, X., Wang, D. and Kr\u00e4henb\u00fchl, P.: Objects as points. (2019) [Online]. https:\/\/ui.adsabs.harvard.edu\/abs\/2019arXiv190407850Z"},{"key":"2901_CR7","doi-asserted-by":"crossref","unstructured":"Bewley, A., Ge, Z., Ott, L., et al.: Simple online and realtime tracking. In: 2016 IEEE international conference on image processing (ICIP). pp. 3464\u20133468 (2016)","DOI":"10.1109\/ICIP.2016.7533003"},{"issue":"1","key":"2901_CR8","first-page":"35","volume":"82","author":"RE Kalman","year":"1960","unstructured":"Kalman, R.E.: A new approach to linear filtering and prediction problems. J. Fluids Eng. 82(1), 35\u201345 (1960)","journal-title":"J. Fluids Eng."},{"key":"2901_CR9","doi-asserted-by":"crossref","unstructured":"Traneva, V., Tranev, S., Atanassova, V.: An intuitionistic fuzzy approach to the Hungarian algorithm. In: Numerical methods and applications: 9th international conference, NMA 2018, Borovets, Bulgaria, August 20\u201324, 2018, Revised Selected Papers. Springer, pp. 167\u201375 (2019)","DOI":"10.1007\/978-3-030-10692-8_19"},{"issue":"3","key":"2901_CR10","doi-asserted-by":"publisher","first-page":"925","DOI":"10.1007\/s00530-022-00895-w","volume":"28","author":"W Guo","year":"2022","unstructured":"Guo, W., Jin, Y., Shan, B., et al.: Multi-cue multi-hypothesis tracking with re-identification for multi-object tracking. Multimedia Syst. 28(3), 925\u2013937 (2022)","journal-title":"Multimedia Syst."},{"issue":"12","key":"2901_CR11","doi-asserted-by":"publisher","first-page":"3660","DOI":"10.1109\/TCSVT.2018.2881123","volume":"29","author":"H Sheng","year":"2018","unstructured":"Sheng, H., Chen, J., Zhang, Y., et al.: Iterative multiple hypothesis tracking with tracklet-level association. IEEE Trans. Circuits Syst. Video Technol.. 29(12), 3660\u20133672 (2018)","journal-title":"IEEE Trans. Circuits Syst. Video Technol.."},{"key":"2901_CR12","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 (2017)","DOI":"10.1109\/ICIP.2017.8296962"},{"issue":"11","key":"2901_CR13","doi-asserted-by":"publisher","first-page":"1757","DOI":"10.3390\/electronics9111757","volume":"9","author":"M G\u00f3mez-Silva","year":"2020","unstructured":"G\u00f3mez-Silva, M., Escalera, A., Armingol, J.M.J.E.: Deep learning of appearance affinity for multi-object tracking and re-identification: a comparative view. Electronics 9(11), 1757 (2020)","journal-title":"Electronics"},{"key":"2901_CR14","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Sun, P., Jiang, Y., et al.: ByteTrack: multi-object tracking by associating every detection box. In: Computer vision\u2013ECCV 2022: 17th European conference. pp. 1\u201321 (2021)","DOI":"10.1007\/978-3-031-20047-2_1"},{"key":"2901_CR15","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zheng, L., Liu, Y., et al.: Towards real-time multi-object tracking. In: European Conference on Computer Vision. pp. 107\u201322 (2020)","DOI":"10.1007\/978-3-030-58621-8_7"},{"key":"2901_CR16","unstructured":"Ren, S., He, K., Girshick, R., et al.: Faster r-cnn: towards real-time object detection with region proposal networks. In: Proceedings of the IEEE international conference on computer vision. pp. 1440\u20131448 (2015)"},{"issue":"11","key":"2901_CR17","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., et al.: Fairmot: on the fairness of detection and re-identification in multiple object tracking. Int. J. Comput. Vision 129(11), 3069\u20133087 (2021)","journal-title":"Int. J. Comput. Vision"},{"key":"2901_CR18","doi-asserted-by":"publisher","unstructured":"Maggiolino, G., Ahmad, A., Cao, J., et al.: Deep OC-SORT: multi-pedestrian tracking by adaptive re-identification. (2023) [Online]. https:\/\/doi.org\/10.48550\/arXiv.2302.11813","DOI":"10.48550\/arXiv.2302.11813"},{"key":"2901_CR19","doi-asserted-by":"publisher","unstructured":"Aharon, N., Orfaig, R. and Bobrovsky, B.Z: BoT-SORT: Robust associations multi-pedestrian tracking. (2022) [Online]. https:\/\/doi.org\/10.48550\/arXiv.2206.14651","DOI":"10.48550\/arXiv.2206.14651"},{"key":"2901_CR20","doi-asserted-by":"crossref","unstructured":"Zhou, K., Yang, Y., Cavallaro, A., et al.: Omni-scale feature learning for person re-identification. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp. 3702\u201312 (2019)","DOI":"10.1109\/ICCV.2019.00380"},{"issue":"3","key":"2901_CR21","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1109\/MSP.2021.3134634","volume":"39","author":"L Ericsson","year":"2022","unstructured":"Ericsson, L., Gouk, H., Loy, C.C., et al.: Self-supervised representation learning: introduction, advances, and challenges. IEEE Signal Process. Mag. 39(3), 42\u201362 (2022)","journal-title":"IEEE Signal Process. Mag."},{"issue":"9","key":"2901_CR22","doi-asserted-by":"publisher","first-page":"1066","DOI":"10.3390\/sym11091066","volume":"11","author":"M Kaya","year":"2019","unstructured":"Kaya, M., Bilge, H.\u015eJ.S.: Deep metric learning: a survey. Symmetry 11(9), 1066 (2019)","journal-title":"Symmetry"},{"key":"2901_CR23","doi-asserted-by":"crossref","unstructured":"Xie, B., Wu, X., Zhang, S., et al.: Learning diverse features with part-level resolution for person re-identification. In: Pattern recognition and computer vision: third chinese conference, PRCV 2020, Nanjing, China, October 16\u201318, 2020, Proceedings, Part III 3. Springer, pp. 16\u201328 (2020)","DOI":"10.1007\/978-3-030-60636-7_2"},{"key":"2901_CR24","doi-asserted-by":"crossref","unstructured":"Sun, Y., Cheng, C., Zhang, Y., et al.: Circle loss: a unified perspective of pair similarity optimization. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 6398\u2013407 (2020)","DOI":"10.1109\/CVPR42600.2020.00643"},{"key":"2901_CR25","doi-asserted-by":"crossref","unstructured":"Cai, Z., Ravichandran, A., Maji, S., et al.: Exponential moving average normalization for self-supervised and semi-supervised learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 194\u2013203 (2021)","DOI":"10.1109\/CVPR46437.2021.00026"},{"issue":"6","key":"2901_CR26","doi-asserted-by":"publisher","first-page":"1142","DOI":"10.1109\/TMM.2017.2652069","volume":"19","author":"W Wang","year":"2017","unstructured":"Wang, W., Yuan, X., Wu, X., et al.: Fast image dehazing method based on linear transformation. IEEE Trans. Multimedia 19(6), 1142\u20131155 (2017)","journal-title":"IEEE Trans. Multimedia"},{"key":"2901_CR27","doi-asserted-by":"crossref","unstructured":"Zheng, L., Shen, L., Tian, L., et al.: Scalable person re-identification: a benchmark. In: Proceedings of the IEEE international conference on computer vision. pp. 1116\u201324 (2015)","DOI":"10.1109\/ICCV.2015.133"},{"key":"2901_CR28","doi-asserted-by":"publisher","first-page":"129169","DOI":"10.1109\/ACCESS.2020.3009852","volume":"8","author":"KH Sun","year":"2020","unstructured":"Sun, K.H., Huh, H., Tama, B.A., et al.: Vision-based fault diagnostics using explainable deep learning with class activation maps. IEEE Access 8, 129169\u2013129179 (2020)","journal-title":"IEEE Access"},{"key":"2901_CR29","unstructured":"Milan, A., Leal-Taixe, L., Reid, I., et al.: MOT16: A benchmark for multi-object tracking. (2016) [Online]. https:\/\/ui.adsabs.harvard.edu\/abs\/2016arXiv160300831M"},{"key":"2901_CR30","unstructured":"Dendorfer, P., Rezatofighi, H., Milan, A., et al.: MOT20: A benchmark for multi object tracking in crowded scenes. (2020) [Online]. https:\/\/ui.adsabs.harvard.edu\/abs\/2020arXiv200309003D"},{"issue":"7","key":"2901_CR31","doi-asserted-by":"publisher","first-page":"9915","DOI":"10.1007\/s11042-022-12095-9","volume":"81","author":"CY Tsai","year":"2022","unstructured":"Tsai, C.Y., Su, Y.K.: MobileNet-JDE: a lightweight multi-object tracking model for embedded systems. Multimedia Tools Appl. 81(7), 9915\u20139937 (2022)","journal-title":"Multimedia Tools Appl."},{"key":"2901_CR32","doi-asserted-by":"publisher","first-page":"105770","DOI":"10.1016\/j.engappai.2022.105770","volume":"119","author":"CY Tsai","year":"2023","unstructured":"Tsai, C.Y., Shen, G.Y., Nisar, H.: Swin-JDE: joint detection and embedding multi-object tracking in crowded scenes based on swin-transformer. Eng. Appl. Artif. Intell. 119, 105770 (2023)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"2901_CR33","doi-asserted-by":"publisher","first-page":"3182","DOI":"10.1109\/TIP.2022.3165376","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 Trans. Image Process. 31, 3182\u20133196 (2022)","journal-title":"IEEE Trans. Image Process."},{"key":"2901_CR34","doi-asserted-by":"crossref","unstructured":"Yu, E., Li, Z., Han, S., et al.: Relationtrack: relation-aware multiple object tracking with decoupled representation. IEEE Transactions on Multimedia. (2022)","DOI":"10.1109\/TMM.2022.3150169"},{"key":"2901_CR35","unstructured":"Sener, O., Koltun, V.: Multi-task learning as multi-objective optimization. In: the 32nd Conference on Neural Information Processing Systems (NeurIPS 2018) (2018)"},{"key":"2901_CR36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2020.3040221","volume":"60","author":"J Zhang","year":"2021","unstructured":"Zhang, J., Xing, M., Sun, G.-C., et al.: Multiple statistics contributing to few-sample deep learning for subtle trace detection in high-resolution SAR images. IEEE Trans. Geosci. Remote Sens. 60, 1\u201314 (2021)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"2901_CR37","doi-asserted-by":"crossref","unstructured":"Du, Y., Wan, J., Zhao, Y., et al.: GIAOTracker: a comprehensive framework for MCMOT with global information and optimizing strategies in VisDrone 2021. In: Proceedings of the IEEE\/CVF International conference on computer vision. pp. 2809\u20132819 (2021)","DOI":"10.1109\/ICCVW54120.2021.00315"},{"key":"2901_CR38","doi-asserted-by":"crossref","unstructured":"Du, Y., Zhao, Z., Song, Y., et al.: Strongsort: Make deepsort great again. IEEE Transactions on Multimedia (2023)","DOI":"10.1109\/TMM.2023.3240881"},{"key":"2901_CR39","doi-asserted-by":"publisher","unstructured":"Cao, J., Weng, X., Khirodkar, R., et al.: Observation-centric sort: Rethinking sort for robust multi-object tracking. (2022) [Online]. https:\/\/doi.org\/10.48550\/arXiv.2203.14360","DOI":"10.48550\/arXiv.2203.14360"},{"key":"2901_CR40","doi-asserted-by":"crossref","unstructured":"Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE computer society conference on computer vision and pattern recognition (CVPR'06). IEEE, pp. 1735\u201342 (2006)","DOI":"10.1109\/CVPR.2006.100"},{"key":"2901_CR41","doi-asserted-by":"crossref","unstructured":"Sun, Y., Zheng, L., Yang, Y., et al.: Beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline). In: Proceedings of the European conference on computer vision (ECCV). pp. 480\u201396 (2018)","DOI":"10.1007\/978-3-030-01225-0_30"},{"key":"2901_CR42","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: A unified embedding for face recognition and clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 815\u201323 (2015)","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"2901_CR43","doi-asserted-by":"publisher","unstructured":"Zhang, X., Luo, H., Fan, X., et al.: Alignedreid: surpassing human-level performance in person re-identification. (2017) [Online]. https:\/\/doi.org\/10.48550\/arXiv.1711.08184","DOI":"10.48550\/arXiv.1711.08184"},{"key":"2901_CR44","doi-asserted-by":"publisher","first-page":"36887","DOI":"10.1109\/ACCESS.2018.2852712","volume":"6","author":"Z Zhang","year":"2018","unstructured":"Zhang, Z., Si, T., Liu, S.J.I.A.: Integration convolutional neural network for person re-identification in camera networks. IEEE Access 6, 36887\u201336896 (2018)","journal-title":"IEEE Access"},{"key":"2901_CR45","doi-asserted-by":"crossref","unstructured":"Zhang, H., Wu, C., Zhang, Z., et al.: ResNeSt: Split-attention networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 2736\u201346 (2022)","DOI":"10.1109\/CVPRW56347.2022.00309"},{"key":"2901_CR46","doi-asserted-by":"crossref","unstructured":"Luo, H., Gu, Y., Liao, X., et al.: Bag of tricks and a strong baseline for deep person re-identification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops (2019)","DOI":"10.1109\/CVPRW.2019.00190"},{"key":"2901_CR47","doi-asserted-by":"publisher","first-page":"1089","DOI":"10.1007\/s00371-020-01854-0","volume":"37","author":"X Zhang","year":"2021","unstructured":"Zhang, X., Wang, X., Gu, C.J.T.V.C.: Online multi-object tracking with pedestrian re-identification and occlusion processing. Vis. Comput. 37, 1089\u20131099 (2021)","journal-title":"Vis. Comput."},{"key":"2901_CR48","doi-asserted-by":"crossref","unstructured":"Ma, N., Zhang, X., Zheng, H.-T., et al.: Shufflenet v2: practical guidelines for efficient cnn architecture design. In: Proceedings of the European conference on computer vision (ECCV). pp. 116\u201331 (2018)","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"2901_CR49","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., et al.: Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4510\u201320 (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"2901_CR50","doi-asserted-by":"crossref","unstructured":"Ding, X., Zhang, X., Han, J., et al.: Scaling up your kernels to 31x31: Revisiting large kernel design in cnns. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 11963\u201375 (2022)","DOI":"10.1109\/CVPR52688.2022.01166"},{"key":"2901_CR51","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., et al.: Cbam: convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV). pp. 3\u201319 (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"issue":"3","key":"2901_CR52","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1109\/TPAMI.2017.2691769","volume":"40","author":"SH Bae","year":"2018","unstructured":"Bae, S.H., Yoon, K.J.: Confidence-based data association and discriminative deep appearance learning for robust online multi-object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 595\u2013610 (2018). https:\/\/doi.org\/10.1109\/TPAMI.2017.2691769","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"2901_CR53","doi-asserted-by":"publisher","first-page":"548","DOI":"10.1007\/s11263-020-01375-2","volume":"129","author":"J Luiten","year":"2021","unstructured":"Luiten, J., Os Ep, A.A., Dendorfer, P., et al.: HOTA: a higher order metric for evaluating multi-object tracking. Int. J. Comput. Vis. 129(2), 548\u2013578 (2021). https:\/\/doi.org\/10.1007\/s11263-020-01375-2","journal-title":"Int. J. Comput. Vis."},{"key":"2901_CR54","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":"2901_CR55","doi-asserted-by":"publisher","unstructured":"Sun, P., Cao, J., Jiang, Y., et al.: Transtrack: Multiple object tracking with transformer. (2020) [online]. https:\/\/doi.org\/10.48550\/arXiv.2012.15460","DOI":"10.48550\/arXiv.2012.15460"},{"key":"2901_CR56","doi-asserted-by":"crossref","unstructured":"Stadler, D., Beyerer, J.: On the performance of crowd-specific detectors in multi-pedestrian tracking. In: 2021 17th IEEE International conference on advanced video and signal based surveillance (AVSS). IEEE, pp. 1\u201312 (2021)","DOI":"10.1109\/AVSS52988.2021.9663836"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-023-02901-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-023-02901-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-023-02901-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T19:37:37Z","timestamp":1729539457000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-023-02901-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,4]]},"references-count":56,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["2901"],"URL":"https:\/\/doi.org\/10.1007\/s00371-023-02901-2","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,4]]},"assertion":[{"value":"10 May 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 June 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any studies with human or animal subjects and all authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}