{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T07:17:22Z","timestamp":1783063042996,"version":"3.54.6"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T00:00:00Z","timestamp":1777680000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T00:00:00Z","timestamp":1783036800000},"content-version":"vor","delay-in-days":62,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Accurate detection and segmentation of moving objects constitute a fundamental challenge in computer vision, particularly for intelligent video surveillance systems operating under variable illumination, dynamic backgrounds, and environmental noise. This paper presents a fully unsupervised dual-phase motion analysis framework that effectively combines statistical independence modeling and geometric contour evolution to achieve high-precision motion detection and segmentation. In the first phase, an enhanced Fast Independent Component Analysis (Fast-ICA) algorithm is employed to perform statistical decomposition of video sequences, exploiting temporal independence to distinguish moving foregrounds from static backgrounds. This process generates an initial motion mask with strong robustness to illumination variation and noise artifacts. In the second phase, a hybrid level set segmentation model integrating the global Chan-Vese formulation and a locally adaptive Yezzi-based energy function refines object boundaries through an adaptive energy minimization process. A stabilization term and a self-regulating convergence criterion are further incorporated to ensure contour smoothness, numerical stability, and resilience to topological changes. Comprehensive experiments conducted on the CDNet-2014 benchmark dataset demonstrate that the proposed method achieves an average recall of 0.9613, precision of 0.9089, and F-measure of 0.9310, outperforming several state-of-the-art supervised, semi-supervised and unsupervised background subtraction algorithms. The proposed Fast-ICA\u2013Level Set fusion framework thus provides a robust, adaptive, and computationally efficient solution for real-world intelligent surveillance and autonomous visual monitoring applications.<\/jats:p>","DOI":"10.1038\/s41598-026-50215-9","type":"journal-article","created":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T09:26:15Z","timestamp":1777713975000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An unsupervised dual-phase framework combining statistical separation and hybrid level set evolution for robust motion detection and segmentation in intelligent video surveillance"],"prefix":"10.1038","volume":"16","author":[{"given":"Meriem","family":"Boumehed","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aykut Fatih","family":"G\u00fcven","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abderrahmane","family":"Naoum","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amir","family":"Merahi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Belal","family":"Alshaqaqi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Khaled","family":"Guerraiche","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muneera","family":"Altayeb","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oleksandr","family":"Rubanenko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,2]]},"reference":[{"key":"50215_CR1","doi-asserted-by":"publisher","first-page":"21627","DOI":"10.1007\/s11042-023-14696-4","volume":"82","author":"YD Teja","year":"2023","unstructured":"Teja, Y. D. Static object detection for video surveillance. Multimedia Tools Appl. 82, 21627\u201321639 (2023).","journal-title":"Multimedia Tools Appl."},{"key":"50215_CR2","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1007\/s13735-022-00227-8","volume":"11","author":"R Patrikar","year":"2022","unstructured":"Patrikar, R. & Parate, M. R. Anomaly detection using edge computing in video surveillance system: A review. Int. J. Multimed. Inf. Retr. 11, 85\u2013110 (2022).","journal-title":"Int. J. Multimed. Inf. Retr."},{"key":"50215_CR3","doi-asserted-by":"publisher","first-page":"4421","DOI":"10.1007\/s11042-020-09838-x","volume":"80","author":"SRR Sanches","year":"2021","unstructured":"Sanches, S. R. R., Sementille, A. C., Aguilar, I. A. & Freire, V. Recommendations for evaluating the performance of background subtraction algorithms for surveillance systems. Multimedia Tools Appl. 80, 4421\u20134454 (2021).","journal-title":"Multimedia Tools Appl."},{"issue":"9","key":"50215_CR4","doi-asserted-by":"publisher","first-page":"4854","DOI":"10.3390\/app13084854","volume":"13","author":"J Ding","year":"2023","unstructured":"Ding, J., Zhang, Z., Yu, X., Zhao, X. & Yan, Z. A novel moving object detection algorithm based on robust image feature threshold segmentation with improved optical flow estimation. Appl. Sci. 13(9), 4854 (2023).","journal-title":"Appl. Sci."},{"key":"50215_CR5","doi-asserted-by":"crossref","unstructured":"Boufares, O., Boussif, M., Saadaoui, W. & Miraoui, I. Moving object detection: a new method combining background subtraction, fuzzy entropy thresholding and differential evolution optimization. Acta Mech. et Automatica, 19, 1, (2025).","DOI":"10.2478\/ama-2025-0013"},{"issue":"5","key":"50215_CR6","doi-asserted-by":"publisher","first-page":"2679","DOI":"10.1109\/JSEN.2014.2382174","volume":"15","author":"S Zhang","year":"2014","unstructured":"Zhang, S., Wang, C., Chan, S.-C., Wei, X. & Ho, C.-H. New object detection, tracking, and recognition approaches for video surveillance over camera network. IEEE Sens. J. 15(5), 2679\u20132691 (2014).","journal-title":"IEEE Sens. J."},{"key":"50215_CR7","doi-asserted-by":"crossref","unstructured":"Yasir, M. & Ali, Y. Review on real-time background extraction: models, applications, environments, challenges and evaluation approaches. IEEE Access, (2021).","DOI":"10.3991\/ijoe.v17i02.18013"},{"key":"50215_CR8","doi-asserted-by":"publisher","first-page":"6077","DOI":"10.1109\/TIP.2019.2922095","volume":"28","author":"Z Fu","year":"2019","unstructured":"Fu, Z. et al. Foreground gating and background refining network for surveillance object detection. IEEE Trans. Image Process. 28, 6077\u20136090 (2019).","journal-title":"IEEE Trans. Image Process."},{"key":"50215_CR9","doi-asserted-by":"crossref","unstructured":"Ren, J. et al. Accurate single stage detector using recurrent rolling convolution, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, pp. 5420\u20135428. (2017).","DOI":"10.1109\/CVPR.2017.87"},{"key":"50215_CR10","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T. & Malik, J. Rich feature hierarchies for accurate object detection and semantic segmentation, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, pp. 580\u2013587. (2014).","DOI":"10.1109\/CVPR.2014.81"},{"issue":"9","key":"50215_CR11","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","volume":"32","author":"PF Felzenszwalb","year":"2009","unstructured":"Felzenszwalb, P. F., Girshick, R. B., McAllester, D. & Ramanan, D. Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627\u20131645 (2009).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"50215_CR12","doi-asserted-by":"crossref","unstructured":"Redmon, J. & Farhadi, A. YOLO9000: better, faster, stronger, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, pp. 7263\u20137271. (2017).","DOI":"10.1109\/CVPR.2017.690"},{"key":"50215_CR13","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3055539","author":"F Gao","year":"2021","unstructured":"Gao, F., Li, Y. & Lu, S. Extracting moving objects more accurately: A CDA contour optimizer. IEEE Trans. Circuits Syst. Video Technol. https:\/\/doi.org\/10.1109\/TCSVT.2021.3055539 (2021).","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"50215_CR14","doi-asserted-by":"publisher","first-page":"53849","DOI":"10.1109\/ACCESS.2021.3071163","volume":"9","author":"MO Tezcan","year":"2021","unstructured":"Tezcan, M. O., Ishwar, P. & Konrad, J. BSUV-Net 2.0: Spatio-temporal data augmentations for video-agnostic supervised background subtraction. IEEE Access 9, 53849\u201353860 (2021).","journal-title":"IEEE Access"},{"key":"50215_CR15","unstructured":"Rahmon, G., Bunyak, F., Seetharaman, G. & Palaniappan, K. Motion U-Net: multi-cue encoder-decoder network for motion segmentation, in Proceedings of the 25th International Conference on Pattern Recognition, (2020)."},{"key":"50215_CR16","unstructured":"Mohammadreza, B., Dinh, D. T. & Rigoll, G. A deep convolutional neural network for video sequence background subtraction. Pattern Recognition, (2017)."},{"key":"50215_CR17","doi-asserted-by":"crossref","unstructured":"Stauffer, C. & Grimson, W. E. L. Adaptive background mixture models for real-time tracking, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 246\u2013252. (1999).","DOI":"10.1109\/CVPR.1999.784637"},{"key":"50215_CR18","doi-asserted-by":"crossref","unstructured":"Zivkovic, Z. Improved adaptive Gaussian mixture model for background subtraction, in Proceedings of the 17th International Conference on Pattern Recognition, vol. 2, pp. 28\u201331. (2004).","DOI":"10.1109\/ICPR.2004.1333992"},{"key":"50215_CR19","doi-asserted-by":"crossref","unstructured":"Martins, I., Carvalho, P., Corte-Real, L. & Alba-Castro, J. L. BMOG: boosted Gaussian mixture model with controlled complexity. Pattern Recognit. Image Analysis, (2017).","DOI":"10.1007\/978-3-319-58838-4_6"},{"key":"50215_CR20","doi-asserted-by":"publisher","DOI":"10.1186\/s41074-017-0036-1","author":"A Varghese","year":"2017","unstructured":"Varghese, A. & Sreelekha, G. Sample-based integrated background subtraction and shadow detection. IPSJ Trans. Comput. Vis. Appl. https:\/\/doi.org\/10.1186\/s41074-017-0036-1 (2017).","journal-title":"IPSJ Trans. Comput. Vis. Appl."},{"key":"50215_CR21","doi-asserted-by":"crossref","unstructured":"Barnich, O. & Van Droogenbroeck, M. ViBe: a powerful random technique to estimate the background in video sequences, in IEEE International Conference on Acoustics, Speech and Signal Processing, Taipei, Taiwan, pp. 945\u2013948. (2009).","DOI":"10.1109\/ICASSP.2009.4959741"},{"key":"50215_CR22","unstructured":"St-Charles, P. L., Bilodeau, G. A. & Bergevin, R. SuBSENSE: a universal change detection method with local adaptive sensitivity. IEEE Trans. Image Processing, (2014)."},{"key":"50215_CR23","doi-asserted-by":"crossref","unstructured":"Jiang, S. & Lu, X. WeSamBE: a weight-sample-based method for background subtraction. IEEE Trans. Circuits Syst. Video Technology, (2017).","DOI":"10.1109\/TCSVT.2017.2711659"},{"issue":"5","key":"50215_CR24","doi-asserted-by":"publisher","DOI":"10.3390\/sym11050621","volume":"11","author":"SH Lee","year":"2019","unstructured":"Lee, S. H., Lee, G. C., Yoo, J. & Kwon, S. WisenetMD: Motion detection using dynamic background region analysis. Symmetry 11(5), 621 (2019).","journal-title":"Symmetry"},{"key":"50215_CR25","doi-asserted-by":"crossref","unstructured":"Cioppa, A., Droogenbroeck, M. V. & Braham, M. Real-time semantic background subtraction. arXiv preprint, (2020).","DOI":"10.1109\/ICIP40778.2020.9190838"},{"issue":"4","key":"50215_CR26","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1007\/BF00133570","volume":"1","author":"M Kass","year":"1988","unstructured":"Kass, M., Witkin, A. & Terzopoulos, D. Snakes: Active contour models. Int. J. Comput. Vision 1(4), 321\u2013331 (1988).","journal-title":"Int. J. Comput. Vision"},{"issue":"10","key":"50215_CR27","doi-asserted-by":"publisher","first-page":"1467","DOI":"10.1109\/83.951533","volume":"10","author":"R Goldenberg","year":"2001","unstructured":"Goldenberg, R., Kimmel, R. & Rudzsky, M. Fast geodesic active contours. IEEE Trans. Image Process. 10 (10), 1467\u20131475 (2001).","journal-title":"IEEE Trans. Image Process."},{"key":"50215_CR28","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.eswa.2015.09.013","volume":"44","author":"M Ciecholewski","year":"2016","unstructured":"Ciecholewski, M. An edge-based active contour model using an inflation\/deflation force with a damping coefficient. Expert Syst. Appl. 44, 22\u201336 (2016).","journal-title":"Expert Syst. Appl."},{"key":"50215_CR29","unstructured":"Mumford, D. & Shah, J. Boundary detection by minimizing functional, in IEEE Conference on Computer Vision and Pattern Recognition, pp. 22\u201326. (1985)."},{"issue":"2","key":"50215_CR30","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1109\/83.902291","volume":"10","author":"T Chan","year":"2001","unstructured":"Chan, T. & Vese, L. Active contours without edges. IEEE Trans. Image Process. 10 (2), 266\u2013277 (2001).","journal-title":"IEEE Trans. Image Process."},{"issue":"1\u20132","key":"50215_CR31","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1006\/jvci.2001.0500","volume":"13","author":"JA Yezzi","year":"2002","unstructured":"Yezzi, J. A., Tsai, A. & Willsky, A. A fully global approach to image segmentation via coupled curve evolution equations. J. Vis. Commun. Image Represent. 13(1\u20132), 195\u2013216 (2002).","journal-title":"J. Vis. Commun. Image Represent."},{"key":"50215_CR32","doi-asserted-by":"publisher","DOI":"10.1007\/s10851-012-0400-9","author":"M Boumehed","year":"2013","unstructured":"Boumehed, M., Alshaqaqi, B., Ouamri, A. & Keche, M. Moving objects localization by local regions based level set: Application on urban traffic. J. Math. Imaging Vis. https:\/\/doi.org\/10.1007\/s10851-012-0400-9 (2013).","journal-title":"J. Math. Imaging Vis."},{"key":"50215_CR33","doi-asserted-by":"publisher","DOI":"10.15866\/irecos.v10i7.6142","author":"B Alshaqaqi","year":"2015","unstructured":"Alshaqaqi, B. & Boumehed, M. New technique for extraction moving object based on active contours for intelligent visual surveillance applications. International Review on Computers and Software (IRECOS) https:\/\/doi.org\/10.15866\/irecos.v10i7.6142 (2015).","journal-title":"International Review on Computers and Software (IRECOS)"},{"key":"50215_CR34","doi-asserted-by":"crossref","unstructured":"Li, C., Kao, C., Gore, J. & Ding, Z. Implicit active contours driven by local binary fitting energy, in IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20137. (2007).","DOI":"10.1109\/CVPR.2007.383014"},{"issue":"12","key":"50215_CR35","doi-asserted-by":"publisher","first-page":"2435","DOI":"10.1016\/j.sigpro.2009.03.014","volume":"89","author":"L Wang","year":"2009","unstructured":"Wang, L., He, L., Mishra, A. & Li, C. Active contours driven by local Gaussian distribution fitting energy. Sig. Process. 89 (12), 2435\u20132447 (2009).","journal-title":"Sig. Process."},{"issue":"3","key":"50215_CR36","doi-asserted-by":"publisher","first-page":"156","DOI":"10.4103\/2228-7477.95284","volume":"1","author":"R Azmi","year":"2011","unstructured":"Azmi, R. & Norozi, N. A new Markov random field segmentation method for breast lesion segmentation in MR images. J. Med. Signals Sens. 1 (3), 156\u2013164 (2011).","journal-title":"J. Med. Signals Sens."},{"key":"50215_CR37","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.neucom.2016.03.050","volume":"205","author":"H Wang","year":"2016","unstructured":"Wang, H., Huang, T. Z. & Zhi, X. A two-stage image segmentation via global and local region active contours. Neurocomputing 205, 130\u2013140 (2016).","journal-title":"Neurocomputing"},{"key":"50215_CR38","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.patrec.2018.01.019","volume":"104","author":"K Ding","year":"2018","unstructured":"Ding, K., Xiao, L. & Weng, G. Active contours driven by local pre-fitting energy for fast image segmentation. Pattern Recognit. Lett. 104, 29\u201336 (2018).","journal-title":"Pattern Recognit. Lett."},{"issue":"9","key":"50215_CR39","doi-asserted-by":"publisher","first-page":"11779","DOI":"10.1007\/s11042-018-6735-5","volume":"78","author":"H Yu","year":"2019","unstructured":"Yu, H., He, F. & Pan, Y. A novel segmentation model for medical images with intensity inhomogeneity based on adaptive perturbation. Multim. Tools Appl. 78(9), 11779\u201311798 (2019).","journal-title":"Multim. Tools Appl."},{"key":"50215_CR40","doi-asserted-by":"publisher","first-page":"5743","DOI":"10.1007\/s11042-019-08493-1","volume":"79","author":"H Yu","year":"2020","unstructured":"Yu, H., He, F. & Pan, Y. A scalable region-based level set method using adaptive bilateral filter for noisy image segmentation. Multim. Tools Appl. 79, 5743\u20135765 (2020).","journal-title":"Multim. Tools Appl."},{"key":"50215_CR41","doi-asserted-by":"publisher","first-page":"59412","DOI":"10.1109\/ACCESS.2020.2981596","volume":"8","author":"H Liu","year":"2020","unstructured":"Liu, H., Fang, J. & Zhang, Z. A novel active contour model guided by global and local signed energy-based pressure force. IEEE Access 8, 59412\u201359426 (2020).","journal-title":"IEEE Access"},{"key":"50215_CR42","doi-asserted-by":"publisher","DOI":"10.1155\/2013\/965419","author":"P Han","year":"2013","unstructured":"Han, P., Du, J., Zhou, J. & Zhu, S. An object detection method using wavelet optical flow and hybrid linear-nonlinear classifier. Math. Probl. Eng. https:\/\/doi.org\/10.1155\/2013\/965419 (2013).","journal-title":"Math. Probl. Eng."},{"key":"50215_CR43","doi-asserted-by":"publisher","DOI":"10.1002\/0471221317","volume-title":"Independent Component Analysis","author":"A Hyv\u00e4rinen","year":"2001","unstructured":"Hyv\u00e4rinen, A., Karhunen, J. & Oja, E. Independent Component Analysis (John Wiley & Sons, 2001)."},{"issue":"3","key":"50215_CR44","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1109\/72.761722","volume":"10","author":"A Hyv\u00e4rinen","year":"1999","unstructured":"Hyv\u00e4rinen, A. Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Netw. 10(3), 626\u2013634 (1999).","journal-title":"IEEE Trans. Neural Netw."},{"key":"50215_CR45","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.cosrev.2014.04.001","volume":"11-12","author":"T Bouwmans","year":"2014","unstructured":"Bouwmans, T. Traditional and recent approaches in background modeling for foreground detection: An overview. Comput. Sci. Rev. 11\u201312, 31\u201366 (2014).","journal-title":"Comput. Sci. Rev."},{"issue":"1","key":"50215_CR46","first-page":"197","volume":"32","author":"A Naoum","year":"2023","unstructured":"Naoum, A., Boumehed, M., Alshaqaqi, B. & Keche, M. Efficient background subtraction method based on fast independent component analysis in video-surveillance. Indones. J. Electr. Eng. Comput. Sci. 32(1), 197\u2013205 (2023).","journal-title":"Indones. J. Electr. Eng. Comput. Sci."},{"key":"50215_CR47","doi-asserted-by":"crossref","unstructured":"Yamazaki, M., Xu, G. & Chen, Y. Detection of moving objects by independent component analysis, in Asian Conference on Computer Vision, pp. 467\u2013478. (2006).","DOI":"10.1007\/11612704_47"},{"key":"50215_CR48","unstructured":"Tsai, D. & Lai, C. Independent component analysis-based background subtraction for indoor surveillance. IEEE Trans. Image Process., (2008)."},{"issue":"7","key":"50215_CR49","doi-asserted-by":"publisher","first-page":"6092","DOI":"10.3390\/s100606092","volume":"10","author":"H Jimenez-Hernandez","year":"2010","unstructured":"Jimenez-Hernandez, H. Background subtraction approach based on independent component analysis. Sensors 10(7), 6092\u20136114 (2010).","journal-title":"Sensors"},{"key":"50215_CR50","doi-asserted-by":"crossref","unstructured":"Sekkati, H., Lagani\u00e8re;, R., Mitiche, A. & Youmaran, R. Robust background subtraction using geodesic active contours in ICA subspace for video surveillance applications, in International Conference on Computer Robot Vision, pp. 190\u2013197. (2012).","DOI":"10.1109\/CRV.2012.33"},{"issue":"5","key":"50215_CR51","first-page":"1020","volume":"33","author":"H Toroghi","year":"2020","unstructured":"Toroghi, H. & Mirzarezaee, M. Najar Araabi foreground extraction using Hilbert-Schmidt independence criterion and particle swarm optimization independent component analysis. Int. J. Eng. 33(5), 1020\u20131026 (2020).","journal-title":"Int. J. Eng."},{"key":"50215_CR52","doi-asserted-by":"crossref","unstructured":"Huang, Y., Jiang, Q. & Qian, Y. A novel method for video moving object detection using improved independent component analysis. IEEE Trans. Circuits Syst. Video Technology, (2020).","DOI":"10.1109\/TCSVT.2020.3023175"},{"key":"50215_CR53","doi-asserted-by":"crossref","unstructured":"Giraldo, J. H. & Bouwmans, T. GraphBGS: background subtraction via recovery of graph signals, in International Conference on Pattern Recognition, Milan, Italy, (2021).","DOI":"10.1109\/ICPR48806.2021.9412999"},{"key":"50215_CR54","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.124709","author":"T Wen","year":"2024","unstructured":"Wen, T., Chen, Z., Zhang, T. & Zou, J. Graph-based semi-supervised learning with non-convex graph total variation regularization. Expert Syst. Appl. https:\/\/doi.org\/10.1016\/j.eswa.2024.124709 (2024).","journal-title":"Expert Syst. Appl."},{"key":"50215_CR55","doi-asserted-by":"crossref","unstructured":"Giraldo, J., Javed, S., Werghi, N. & Bouwmans, T. GraphCNN for moving object detection in complex environments from unseen videos, in Fourth Workshop on Robust Subspace Learning and Computer Vision, (2021).","DOI":"10.1109\/ICCVW54120.2021.00030"},{"key":"50215_CR56","doi-asserted-by":"crossref","unstructured":"Prummel, W., Giraldo, J. H., Zakharova, A. & Bouwmans, T. Inductive graph neural networks for moving object segmentation, in IEEE International Conference on Image Processing, Kuala Lumpur, Malaysia, (2023).","DOI":"10.1109\/ICIP49359.2023.10222668"},{"issue":"5","key":"50215_CR57","first-page":"2485","volume":"44","author":"J Giraldo","year":"2022","unstructured":"Giraldo, J., Javed, S. & Bouwmans, T. Graph moving object segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 44(5), 2485\u20132503 (2022).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"7","key":"50215_CR58","doi-asserted-by":"publisher","first-page":"5990","DOI":"10.1109\/TPAMI.2025.3559891","volume":"47","author":"Z Jin","year":"2025","unstructured":"Jin, Z., Qiu, Y., Zhang, K., Li, H. & Luo, W. MB-TaylorFormer V2: Improved multi-branch linear transformer expanded by Taylor formula for image restoration. IEEE Trans. Pattern Anal. Mach. Intell. 47(7), 5990\u20136005 (2025).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"50215_CR59","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1109\/TIP.2018.2867733","volume":"28","author":"K Zhang","year":"2019","unstructured":"Zhang, K., Luo, W., Zhong, Y., Ma, L. & Liu, W. Adversarial spatio-temporal learning for video deblurring. IEEE Trans. Image Process. 28(1), 291\u2013301 (2019).","journal-title":"IEEE Trans. Image Process."},{"issue":"1","key":"50215_CR60","first-page":"99","volume":"1","author":"K Zhang","year":"2023","unstructured":"Zhang, K. et al. MC-Blur: A Comprehensive Benchmark for Image Deblurring. IEEE Trans. Circuits Syst. Video Technol. 1 (1), 99 (2023).","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"50215_CR61","unstructured":"\/www2.eecs.berkeley.edu\/Research\/Projects\/CS\/vision\/bsds\/\"http:\/\/\/www2.eecs.berkeley.edu\/Research\/Projects\/CS\/vision\/bsds\/\"\/>"},{"key":"50215_CR62","doi-asserted-by":"crossref","unstructured":"Wang, Y. et al. CDnet-2014: An Expanded Change Detection Benchmark Dataset, Proceeding IEEE Workshop on Change Detection (CDW-2014) at CVPR-2014, pp. 387\u2013394, (2014).","DOI":"10.1109\/CVPRW.2014.126"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-50215-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-50215-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-50215-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T06:40:50Z","timestamp":1783060850000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-50215-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,2]]},"references-count":62,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["50215"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-50215-9","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,2]]},"assertion":[{"value":"10 December 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 May 2026","order":3,"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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"20367"}}