{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T14:43:52Z","timestamp":1777041832710,"version":"3.51.4"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,8,7]],"date-time":"2023-08-07T00:00:00Z","timestamp":1691366400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,8,7]],"date-time":"2023-08-07T00:00:00Z","timestamp":1691366400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100010668","name":"H2020 Leadership in Enabling and Industrial Technologies","doi-asserted-by":"publisher","award":["954040"],"award-info":[{"award-number":["954040"]}],"id":[{"id":"10.13039\/100010668","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010801","name":"Xunta de Galicia","doi-asserted-by":"publisher","award":["ED431F 2021\/11"],"award-info":[{"award-number":["ED431F 2021\/11"]}],"id":[{"id":"10.13039\/501100010801","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010801","name":"Xunta de Galicia","doi-asserted-by":"publisher","award":["ED431G 2019\/01"],"award-info":[{"award-number":["ED431G 2019\/01"]}],"id":[{"id":"10.13039\/501100010801","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010801","name":"Xunta de Galicia","doi-asserted-by":"publisher","award":["ED431C 2021\/30"],"award-info":[{"award-number":["ED431C 2021\/30"]}],"id":[{"id":"10.13039\/501100010801","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004837","name":"Ministerio de Ciencia e Innovaci\u00f3n","doi-asserted-by":"publisher","award":["PID2019-104184RB-I00"],"award-info":[{"award-number":["PID2019-104184RB-I00"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004837","name":"Ministerio de Ciencia e Innovaci\u00f3n","doi-asserted-by":"publisher","award":["AEI\/RYC2018-025385-I"],"award-info":[{"award-number":["AEI\/RYC2018-025385-I"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2024,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Real-time play recognition and classification algorithms are crucial for automating video production and live broadcasts of sporting events. However, current methods relying on human pose estimation and deep neural networks introduce high latency on commodity hardware, limiting their usability in low-cost real-time applications. We present PlayNet, a novel approach to real-time handball play classification. Our method is based on Kalman embeddings, a new low-dimensional representation for game states that enables efficient operation on commodity hardware and customized camera layouts. Firstly, we leverage Kalman filtering to detect and track the main agents in the playing field, allowing us to represent them in a single normalized coordinate space. Secondly, we utilize a neural network trained in nonlinear dimensionality reduction through fuzzy topological data structure analysis. As a result, PlayNet achieves real-time play classification with under 55\u00a0ms of latency on commodity hardware, making it a promising addition to automated live broadcasting and game analysis pipelines.\n<\/jats:p>","DOI":"10.1007\/s00371-023-02972-1","type":"journal-article","created":{"date-parts":[[2023,8,7]],"date-time":"2023-08-07T21:01:29Z","timestamp":1691442089000},"page":"2695-2711","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["PlayNet: real-time handball play classification with Kalman embeddings and neural networks"],"prefix":"10.1007","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6042-3588","authenticated-orcid":false,"given":"Omar A.","family":"Mures","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1309-7086","authenticated-orcid":false,"given":"Javier","family":"Taibo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6864-3737","authenticated-orcid":false,"given":"Emilio J.","family":"Padr\u00f3n","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0817-1010","authenticated-orcid":false,"given":"Jose A.","family":"Iglesias-Guitian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,7]]},"reference":[{"issue":"3","key":"2972_CR1","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1561\/2200000090","volume":"14","author":"A Agrawal","year":"2021","unstructured":"Agrawal, A., Ali, A., Boyd, S.: Minimum-distortion embedding. Found. Trends Mach. Learn. 14(3), 211\u2013378 (2021). https:\/\/doi.org\/10.1561\/2200000090","journal-title":"Found. Trends Mach. Learn."},{"issue":"6\u20138","key":"2972_CR2","doi-asserted-by":"publisher","first-page":"1013","DOI":"10.1007\/s00371-019-01673-y","volume":"35","author":"M Ali","year":"2019","unstructured":"Ali, M., Jones, M.W., Xie, X., Williams, M.: Timecluster: dimension reduction applied to temporal data for visual analytics. Vis. Comput. 35(6\u20138), 1013\u20131026 (2019). https:\/\/doi.org\/10.1007\/s00371-019-01673-y","journal-title":"Vis. Comput."},{"key":"2972_CR3","doi-asserted-by":"publisher","unstructured":"Biermann, H., Theiner, J., Bassek, M., Raabe, D., Memmert, D., Ewerth, R.: A unified taxonomy and multimodal dataset for events in invasion games. In: Proceedings of the 4th International Workshop on Multimedia Content Analysis in Sports, pp. 1\u201310 (2021). https:\/\/doi.org\/10.48550\/arXiv.2108.11149","DOI":"10.48550\/arXiv.2108.11149"},{"key":"2972_CR4","doi-asserted-by":"publisher","unstructured":"Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: YOLOv4: Optimal speed and accuracy of object detection (2020). https:\/\/doi.org\/10.48550\/arXiv.2004.10934","DOI":"10.48550\/arXiv.2004.10934"},{"key":"2972_CR5","doi-asserted-by":"publisher","unstructured":"Carr, P., Mistry, M., Matthews, I.: Hybrid robotic\/virtual pan-tilt-zom cameras for autonomous event recording. In: Proceedings of the 21st ACM international conference on Multimedia, pp. 193\u2013202 (2013). https:\/\/doi.org\/10.1145\/2502081.2502086","DOI":"10.1145\/2502081.2502086"},{"issue":"2","key":"2972_CR6","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1007\/s00138-022-01283-0","volume":"33","author":"H Carrillo","year":"2022","unstructured":"Carrillo, H., Quiroga, J., Zapata, L., Maldonado, E.: Automatic football video production system with edge processing. Mach. Vis. Appl. 33(2), 32 (2022). https:\/\/doi.org\/10.1007\/s00138-022-01283-0","journal-title":"Mach. Vis. Appl."},{"key":"2972_CR7","doi-asserted-by":"publisher","unstructured":"Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system 785\u2013794 (2016). https:\/\/doi.org\/10.1145\/2939672.2939785","DOI":"10.1145\/2939672.2939785"},{"key":"2972_CR8","doi-asserted-by":"publisher","unstructured":"Choukroun, Y., Kravchik, E., Yang, F., Kisilev, P.: Low-bit quantization of neural networks for efficient inference. In: Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 3009\u20133018. IEEE (2019). https:\/\/doi.org\/10.1109\/ICCVW.2019.00363","DOI":"10.1109\/ICCVW.2019.00363"},{"issue":"1","key":"2972_CR9","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","volume":"13","author":"T Cover","year":"1967","unstructured":"Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21\u201327 (1967). https:\/\/doi.org\/10.1109\/TIT.1967.1053964","journal-title":"IEEE Trans. Inf. Theory"},{"issue":"8","key":"2972_CR10","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1016\/S0262-8856(98)00183-8","volume":"17","author":"A Criminisi","year":"1999","unstructured":"Criminisi, A., Reid, I., Zisserman, A.: A plane measuring device. Image Vis. Comput. 17(8), 625\u2013634 (1999). https:\/\/doi.org\/10.1016\/S0262-8856(98)00183-8","journal-title":"Image Vis. Comput."},{"issue":"39","key":"2972_CR11","doi-asserted-by":"publisher","first-page":"29685","DOI":"10.1007\/s11042-020-09409-0","volume":"79","author":"C Cuevas","year":"2020","unstructured":"Cuevas, C., Quilon, D., Garc\u00eda, N.: Techniques and applications for soccer video analysis: a survey. Multimed. Tools Appl. 79(39), 29685\u201329721 (2020). https:\/\/doi.org\/10.1007\/s11042-020-09409-0","journal-title":"Multimed. Tools Appl."},{"key":"2972_CR12","doi-asserted-by":"publisher","unstructured":"Deliege, A., Cioppa, A., Giancola, S., Seikavandi, M.J., Dueholm, J.V., Nasrollahi, K., Ghanem, B., Moeslund, T.B., Van\u00a0Droogenbroeck, M.: Soccernet-v2: A dataset and benchmarks for holistic understanding of broadcast soccer videos. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4508\u20134519 (2021). https:\/\/doi.org\/10.48550\/arXiv.2011.13367","DOI":"10.48550\/arXiv.2011.13367"},{"key":"2972_CR13","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","volume":"63","author":"P Geurts","year":"2006","unstructured":"Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. 63, 3\u201342 (2006). https:\/\/doi.org\/10.1007\/s10994-006-6226-1","journal-title":"Mach. Learn."},{"issue":"1","key":"2972_CR14","doi-asserted-by":"publisher","first-page":"99","DOI":"10.5565\/rev\/elcvia.1286","volume":"20","author":"C Guntuboina","year":"2021","unstructured":"Guntuboina, C., Porwal, A., Jain, P., Shingrakhia, H.: Deep learning based automated sports video summarization using YOLO. Electron. Lett. Comput. Vis. Image Anal. 20(1), 99\u2013116 (2021). https:\/\/doi.org\/10.5565\/rev\/elcvia.1286","journal-title":"Electron. Lett. Comput. Vis. Image Anal."},{"key":"2972_CR15","doi-asserted-by":"publisher","unstructured":"Ho, T.K.: Random decision forests. In: Proceedings of 3rd international conference on document analysis and recognition, vol.\u00a01, pp. 278\u2013282. IEEE (1995). https:\/\/doi.org\/10.1109\/ICDAR.1995.598994","DOI":"10.1109\/ICDAR.1995.598994"},{"issue":"8","key":"2972_CR16","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997). https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput."},{"key":"2972_CR17","doi-asserted-by":"publisher","unstructured":"Ivasic-Kos, M., Host, K., Pobar, M.: Application of deep learning methods for detection and tracking of players. In: P.L. Mazzeo, P.\u00a0Spagnolo (eds.) Deep Learning Applications. IntechOpen (2021). https:\/\/doi.org\/10.5772\/intechopen.96308","DOI":"10.5772\/intechopen.96308"},{"issue":"1","key":"2972_CR18","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1115\/1.3658902","volume":"83","author":"RE Kalman","year":"1961","unstructured":"Kalman, R.E., Bucy, R.S.: New results in linear filtering and prediction theory. J. Basic Eng. 83(1), 95\u2013108 (1961). https:\/\/doi.org\/10.1115\/1.3658902","journal-title":"J. Basic Eng."},{"key":"2972_CR19","doi-asserted-by":"publisher","first-page":"1662","DOI":"10.1109\/ACCESS.2017.2779939","volume":"6","author":"F Karim","year":"2017","unstructured":"Karim, F., Majumdar, S., Darabi, H., Chen, S.: Lstm fully convolutional networks for time series classification. IEEE Access 6, 1662\u20131669 (2017). https:\/\/doi.org\/10.1109\/ACCESS.2017.2779939","journal-title":"IEEE Access"},{"key":"2972_CR20","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., Liu, T.Y.: Lightgbm: a highly efficient gradient boosting decision tree. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"2972_CR21","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1007\/s11042-015-3058-7","volume":"76","author":"L Leng","year":"2017","unstructured":"Leng, L., Li, M., Kim, C., Bi, X.: Dual-source discrimination power analysis for multi-instance contactless palmprint recognition. Multimed. Tools Appl. 76, 333\u2013354 (2017). https:\/\/doi.org\/10.1007\/s11042-015-3058-7","journal-title":"Multimed. Tools Appl."},{"key":"2972_CR22","doi-asserted-by":"publisher","unstructured":"Leng, L., Zhang, J., Chen, G., Khan, M.K., Alghathbar, K.: Two-directional two-dimensional random projection and its variations for face and palmprint recognition. In: Computational Science and Its Applications - ICCSA 2011, Lecture Notes in Computer Science, vol. 6786, pp. 458\u2013470. Springer (2011). https:\/\/doi.org\/10.1007\/978-3-642-21934-4_37","DOI":"10.1007\/978-3-642-21934-4_37"},{"key":"2972_CR23","first-page":"1135","volume":"7","author":"T Liu","year":"2006","unstructured":"Liu, T., Moore, A.W., Gray, A.: New algorithms for efficient high-dimensional nonparametric classification. J. Mach. Learn. Res. 7, 1135\u20131158 (2006)","journal-title":"J. Mach. Learn. Res."},{"key":"2972_CR24","unstructured":"Maaten, L.v.d., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579\u20132605 (2008). http:\/\/jmlr.org\/papers\/v9\/vandermaaten08a.html"},{"key":"2972_CR25","doi-asserted-by":"publisher","unstructured":"McInnes, L., Healy, J., Saul, N., Gro\u00dfberger, L.: UMAP: Uniform manifold approximation and projection. J. Open Source Softw. 3(29), 861 (2018). https:\/\/doi.org\/10.21105\/joss.00861","DOI":"10.21105\/joss.00861"},{"issue":"3","key":"2972_CR26","doi-asserted-by":"publisher","first-page":"790","DOI":"10.1016\/j.compeleceng.2012.11.020","volume":"39","author":"E Mendi","year":"2013","unstructured":"Mendi, E., Clemente, H.B., Bayrak, C.: Sports video summarization based on motion analysis. Comput. Electr. Eng. 39(3), 790\u2013796 (2013). https:\/\/doi.org\/10.1016\/j.compeleceng.2012.11.020","journal-title":"Comput. Electr. Eng."},{"key":"2972_CR27","doi-asserted-by":"publisher","unstructured":"Morra, L., Manigrasso, F., Canto, G., Gianfrate, C., Guarino, E., Lamberti, F.: Slicing and dicing soccer: automatic detection of complex events from spatio-temporal data. In: Image Analysis and Recognition - ICIAR 2020, Lecture Notes in Computer Science, vol. 12131, pp. 107\u2013121. Springer (2020). https:\/\/doi.org\/10.1007\/978-3-030-50347-5_11","DOI":"10.1007\/978-3-030-50347-5_11"},{"key":"2972_CR28","doi-asserted-by":"publisher","unstructured":"M\u00fcller, O., Caron, M., D\u00f6ring, M., Heuwinkel, T., Baumeister, J.: PIVOT: a parsimonious end-to-end learning framework for valuing player actions in handball using tracking data. In: Proceedings of the International Workshop on Machine Learning and Data Mining for Sports Analytics (MLSA 2021), Communications in Computer and Information Science, vol. 1571, pp. 116\u2013128. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-02044-5_10","DOI":"10.1007\/978-3-031-02044-5_10"},{"key":"2972_CR29","doi-asserted-by":"publisher","unstructured":"Norg\u00e5rd\u00a0Rongved, O.A., Hicks, S.A., Thambawita, V., Stensland, H.K., Zouganeli, E., Johansen, D., Riegler, M.A., Halvorsen, P.: Real-time detection of events in soccer videos using 3D convolutional neural networks. In: Proceedings of the 2020 IEEE International Symposium on Multimedia (ISM 2020), pp. 135\u2013144. IEEE (2020). https:\/\/doi.org\/10.1109\/ISM.2020.00030","DOI":"10.1109\/ISM.2020.00030"},{"key":"2972_CR30","doi-asserted-by":"publisher","first-page":"116321","DOI":"10.1109\/ACCESS.2020.3004182","volume":"8","author":"M Oytun","year":"2020","unstructured":"Oytun, M., Tinazci, C., Sekeroglu, B., Acikada, C., Yavuz, H.U.: Performance prediction and evaluation in female handball players using machine learning models. IEEE Access 8, 116321\u2013116335 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3004182","journal-title":"IEEE Access"},{"key":"2972_CR31","doi-asserted-by":"publisher","unstructured":"Poli\u010dar, P.G., Stra\u017ear, M., Zupan, B.: openTSNE: a modular Python library for t-SNE dimensionality reduction and embedding. bioRxiv preprint (2019). https:\/\/doi.org\/10.1101\/731877","DOI":"10.1101\/731877"},{"key":"2972_CR32","doi-asserted-by":"publisher","unstructured":"Quiroga, J., Carrillo, H., Maldonado, E., Ruiz, J., Zapata, L.M.: As seen on TV: automatic basketball video production using gaussian-based actionness and game states recognition. In: Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 3911\u20133920. IEEE (2020). https:\/\/doi.org\/10.1109\/CVPRW50498.2020.00455","DOI":"10.1109\/CVPRW50498.2020.00455"},{"key":"2972_CR33","doi-asserted-by":"publisher","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S.: Generalized intersection over union: A metric and a loss for bounding box regression. In: Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019), pp. 658\u2013666. IEEE (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00075","DOI":"10.1109\/CVPR.2019.00075"},{"issue":"2","key":"2972_CR34","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1007\/s11554-014-0406-1","volume":"13","author":"M Schlipsing","year":"2017","unstructured":"Schlipsing, M., Salmen, J., Tschentscher, M., Igel, C.: Adaptive pattern recognition in real-time video-based soccer analysis. J. Real Time Image Proc. 13(2), 345\u2013361 (2017). https:\/\/doi.org\/10.1007\/s11554-014-0406-1","journal-title":"J. Real Time Image Proc."},{"issue":"5","key":"2972_CR35","doi-asserted-by":"publisher","first-page":"1212","DOI":"10.1109\/TCSVT.2017.2655624","volume":"28","author":"HC Shih","year":"2017","unstructured":"Shih, H.C.: A survey of content-aware video analysis for sports. IEEE Trans. Circuits Syst. Video Technol. 28(5), 1212\u20131231 (2017). https:\/\/doi.org\/10.1109\/TCSVT.2017.2655624","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"7","key":"2972_CR36","doi-asserted-by":"publisher","first-page":"2285","DOI":"10.1007\/s00371-021-02111-8","volume":"38","author":"H Shingrakhia","year":"2022","unstructured":"Shingrakhia, H., Patel, H.: Sgrnn-am and HRF-DBN: a hybrid machine learning model for cricket video summarization. Vis. Comput. 38(7), 2285\u20132301 (2022). https:\/\/doi.org\/10.1007\/s00371-021-02111-8","journal-title":"Vis. Comput."},{"issue":"1","key":"2972_CR37","doi-asserted-by":"publisher","first-page":"8914","DOI":"10.1038\/s41598-019-45301-0","volume":"9","author":"B Szubert","year":"2019","unstructured":"Szubert, B., Cole, J.E., Monaco, C., Drozdov, I.: Structure-preserving visualisation of high dimensional single-cell datasets. Sci. Rep. 9(1), 8914 (2019). https:\/\/doi.org\/10.1038\/s41598-019-45301-0","journal-title":"Sci. Rep."},{"key":"2972_CR38","doi-asserted-by":"publisher","unstructured":"Taud, H., Mas, J.: Multilayer perceptron (mlp). Geomatic approaches for modeling land change scenarios, pp. 451\u2013455 (2018). https:\/\/doi.org\/10.1007\/978-3-319-60801-3_27","DOI":"10.1007\/978-3-319-60801-3_27"},{"issue":"2","key":"2972_CR39","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1109\/TCSVT.2013.2243640","volume":"24","author":"M Tavassolipour","year":"2014","unstructured":"Tavassolipour, M., Karimian, M., Kasaei, S.: Event detection and summarization in soccer videos using Bayesian network and copula. IEEE Trans. Circuits Syst. Video Technol. 24(2), 291\u2013304 (2014). https:\/\/doi.org\/10.1109\/TCSVT.2013.2243640","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"7","key":"2972_CR40","doi-asserted-by":"publisher","first-page":"2288","DOI":"10.3390\/s21072288","volume":"21","author":"R van den Tillaar","year":"2021","unstructured":"van den Tillaar, R., Bhandurge, S., Stewart, T.: Can machine learning with IMUs be used to detect different throws and estimate ball velocity in team handball? Sensors 21(7), 2288 (2021). https:\/\/doi.org\/10.3390\/s21072288. (Part of special issue: Sensors in Sports Biomechanics)","journal-title":"Sensors"},{"key":"2972_CR41","doi-asserted-by":"publisher","unstructured":"Verucchi, M., Brilli, G., Sapienza, D., Verasani, M., Arena, M., Gatti, F., Capotondi, A., Cavicchioli, R., Bertogna, M., Solieri, M.: A systematic assessment of embedded neural networks for object detection. In: Proceedings of the 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2020), pp. 937\u2013944. IEEE (2020). https:\/\/doi.org\/10.1109\/ETFA46521.2020.9212130","DOI":"10.1109\/ETFA46521.2020.9212130"},{"issue":"1\u20133","key":"2972_CR42","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/0169-7439(87)80084-9","volume":"2","author":"S Wold","year":"1987","unstructured":"Wold, S., Esbensen, K., Geladi, P.: Principal component analysis. Chemom. Intell. Lab. Syst. 2(1\u20133), 37\u201352 (1987). https:\/\/doi.org\/10.1016\/0169-7439(87)80084-9","journal-title":"Chemom. Intell. Lab. Syst."},{"issue":"2","key":"2972_CR43","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/S0893-6080(05)80023-1","volume":"5","author":"DH Wolpert","year":"1992","unstructured":"Wolpert, D.H.: Stacked generalization. Neural Netw. 5(2), 241\u2013259 (1992). https:\/\/doi.org\/10.1016\/S0893-6080(05)80023-1","journal-title":"Neural Netw."},{"issue":"1","key":"2972_CR44","doi-asserted-by":"publisher","first-page":"162","DOI":"10.21629\/JSEE.2017.01.18","volume":"28","author":"B Zhao","year":"2017","unstructured":"Zhao, B., Lu, H., Chen, S., Liu, J., Wu, D.: Convolutional neural networks for time series classification. J. Syst. Eng. Electron. 28(1), 162\u2013169 (2017). https:\/\/doi.org\/10.21629\/JSEE.2017.01.18","journal-title":"J. Syst. Eng. Electron."},{"key":"2972_CR45","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1007\/s00371-019-01652-3","volume":"36","author":"M Zolfaghari","year":"2020","unstructured":"Zolfaghari, M., Ghanei-Yakhdan, H., Yazdi, M.: Real-time object tracking based on an adaptive transition model and extended Kalman filter to handle full occlusion. Vis. Comput. 36, 701\u2013715 (2020). https:\/\/doi.org\/10.1007\/s00371-019-01652-3","journal-title":"Vis. Comput."}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-023-02972-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-023-02972-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-023-02972-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T17:12:31Z","timestamp":1712337151000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-023-02972-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,7]]},"references-count":45,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["2972"],"URL":"https:\/\/doi.org\/10.1007\/s00371-023-02972-1","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,7]]},"assertion":[{"value":"13 June 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 August 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":"The author certifies that there is no conflict of interest with any individual\/organization for the present work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}