{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T22:33:42Z","timestamp":1777502022562,"version":"3.51.4"},"reference-count":61,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T00:00:00Z","timestamp":1747699200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T00:00:00Z","timestamp":1747699200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J. King Saud Univ. Comput. Inf. Sci."],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s44443-025-00044-z","type":"journal-article","created":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T15:37:04Z","timestamp":1747755424000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["SFG-YOLOv8: efficient and lightweight small-feature gesture keypoint detector"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-3136-5486","authenticated-orcid":false,"given":"Weimin","family":"Che","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8467-8852","authenticated-orcid":false,"given":"Hui","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2080-2213","authenticated-orcid":false,"given":"Bangxue","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-6559-1289","authenticated-orcid":false,"given":"Qun","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1029-2470","authenticated-orcid":false,"given":"Hongji","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8727-1915","authenticated-orcid":false,"given":"Shilong","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6309-0410","authenticated-orcid":false,"given":"Hongcheng","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,20]]},"reference":[{"key":"44_CR1","doi-asserted-by":"publisher","unstructured":"Alimam H, Mazzuto G, Tozzi N, Emanuele Ciarapica F, Bevilacqua M (2023) The resurrection of digital triplet: a cognitive pillar of human-machine integration at the dawn of industry 5.0. J King Saud Univ Comput Inf Sci 35:101846. https:\/\/doi.org\/10.1016\/j.jksuci.2023.101846","DOI":"10.1016\/j.jksuci.2023.101846"},{"key":"44_CR2","doi-asserted-by":"crossref","unstructured":"Baek S, Kim KI, Kim T-K (2018) Augmented skeleton space transfer for depth-based hand pose estimation. In: 2018 IEEE\/CVF conference on computer vision and pattern recognition. pp 8330\u20138339","DOI":"10.1109\/CVPR.2018.00869"},{"key":"44_CR3","doi-asserted-by":"publisher","first-page":"106203","DOI":"10.1016\/j.bspc.2024.106203","volume":"93","author":"A Bahuguna","year":"2024","unstructured":"Bahuguna A, Bhaumik G, Govil MC (2024) Local extrema min-max pattern: a novel descriptor for extracting compact and discrete features for hand gesture recognition. Biomed Signal Process Control 93:106203. https:\/\/doi.org\/10.1016\/j.bspc.2024.106203","journal-title":"Biomed Signal Process Control"},{"key":"44_CR4","doi-asserted-by":"publisher","first-page":"1949","DOI":"10.1109\/ICCV.2015.226","volume":"2015","author":"S Bambach","year":"2015","unstructured":"Bambach S, Lee S, Crandall DJ, Yu C (2015) Lending a hand: detecting hands and recognizing activities in complex egocentric interactions. Proc, IEEE Int Conf Comput vis 2015:1949\u20131957. https:\/\/doi.org\/10.1109\/ICCV.2015.226","journal-title":"Proc, IEEE Int Conf Comput vis"},{"key":"44_CR5","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1109\/TPAMI.2019.2929257","volume":"43","author":"Z Cao","year":"2021","unstructured":"Cao Z, Hidalgo G, Simon T, Wei S-E, Sheikh Y (2021) OpenPose: realtime multi-person 2D pose estimation using part affinity fields. IEEE Trans Pattern Anal Mach Intell 43:172\u2013186. https:\/\/doi.org\/10.1109\/TPAMI.2019.2929257","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"44_CR6","doi-asserted-by":"crossref","unstructured":"Chen Y, Wang Z, Peng Y, Zhang Z, Yu G, Sun J (2018) Cascaded pyramid network for multi-person pose estimation. In: 2018 IEEE\/CVF conference on computer vision and pattern recognition. pp 7103\u20137112","DOI":"10.1109\/CVPR.2018.00742"},{"key":"44_CR7","doi-asserted-by":"publisher","unstructured":"Chollet F (2017) Xception: deep learning with depthwise separable convolutions. 2017 IEEE conference on Computer Vision and Pattern Recognition (CVPR) 1800\u20131807.\u00a0https:\/\/doi.org\/10.1109\/CVPR.2017.195","DOI":"10.1109\/CVPR.2017.195"},{"key":"44_CR8","doi-asserted-by":"publisher","first-page":"6200","DOI":"10.1080\/10447318.2023.2247614","volume":"40","author":"C Creed","year":"2024","unstructured":"Creed C, Al-Kalbani M, Theil A, Sarcar S, Williams I (2024) Inclusive augmented and virtual reality: a research agenda. Int J Human-Comput Inter 40:6200\u20136219. https:\/\/doi.org\/10.1080\/10447318.2023.2247614","journal-title":"Int J Human-Comput Inter"},{"key":"44_CR9","doi-asserted-by":"publisher","unstructured":"Dang Q, Yin J, Wang B, Zheng W (2019) Deep learning based 2D human pose estimation: a survey. Tsinghua Sci Technol 24:663\u2013676. https:\/\/doi.org\/10.26599\/TST.2018.9010100","DOI":"10.26599\/TST.2018.9010100"},{"key":"44_CR10","doi-asserted-by":"publisher","first-page":"4552","DOI":"10.1007\/s00034-020-01384-z","volume":"39","author":"A Daoui","year":"2020","unstructured":"Daoui A, Yamni M, Ogri OE, Karmouni H, Sayyouri M, Qjidaa H (2020) New algorithm for large-sized 2D and 3D image reconstruction using higher-order hahn moments. Circuits Syst Signal Process 39:4552\u20134577. https:\/\/doi.org\/10.1007\/s00034-020-01384-z","journal-title":"Circuits Syst Signal Process"},{"key":"44_CR11","doi-asserted-by":"publisher","first-page":"107854","DOI":"10.1016\/j.sigpro.2020.107854","volume":"180","author":"A Daoui","year":"2021","unstructured":"Daoui A, Yamni M, Karmouni H, Sayyouri M, Qjidaa H (2021) Biomedical signals reconstruction and zero-watermarking using separable fractional order charlier\u2013krawtchouk transformation and sine cosine algorithm. Signal Process 180:107854. https:\/\/doi.org\/10.1016\/j.sigpro.2020.107854","journal-title":"Signal Process"},{"key":"44_CR12","doi-asserted-by":"publisher","first-page":"6449","DOI":"10.1007\/s00371-023-03175-4","volume":"40","author":"AK Dash","year":"2024","unstructured":"Dash AK, Balaji KV, Dogra DP, Kim B-G (2024) Interactions with 3D virtual objects in augmented reality using natural gestures. Visual Comput 40:6449\u20136462. https:\/\/doi.org\/10.1007\/s00371-023-03175-4","journal-title":"Visual Comput"},{"key":"44_CR13","doi-asserted-by":"publisher","unstructured":"Doosti B (2019) Hand pose estimation: a survey. https:\/\/doi.org\/10.48550\/arXiv.1903.01013","DOI":"10.48550\/arXiv.1903.01013"},{"key":"44_CR14","doi-asserted-by":"publisher","unstructured":"Ge Z, Liu S, Wang F, Li Z, Sun J (2021) YOLOX: Exceeding YOLO Series in 2021. arXiv e-prints. https:\/\/doi.org\/10.48550\/arXiv.2107.08430","DOI":"10.48550\/arXiv.2107.08430"},{"key":"44_CR15","doi-asserted-by":"publisher","first-page":"108674","DOI":"10.1016\/j.patcog.2022.108674","volume":"128","author":"D Gupta","year":"2022","unstructured":"Gupta D, Artacho B, Savakis A (2022) HandyPose: multi-level framework for hand pose estimation. Pattern Recognit 128:108674. https:\/\/doi.org\/10.1016\/j.patcog.2022.108674","journal-title":"Pattern Recognit"},{"key":"44_CR16","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"44_CR17","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Doll\u00e1r P, Girshick R (2017) Mask R-CNN. In: 2017 IEEE International Conference on Computer Vision (ICCV). pp 2980\u20132988","DOI":"10.1109\/ICCV.2017.322"},{"key":"44_CR18","doi-asserted-by":"publisher","first-page":"13026","DOI":"10.1117\/1.JEI.23.1.013026","volume":"23","author":"A Hmimid","year":"2014","unstructured":"Hmimid A, Sayyouri M, Qjidaa H (2014) Image classification using a new set of separable two-dimensional discrete orthogonal invariant moments. J Electron Imaging 23:13026. https:\/\/doi.org\/10.1117\/1.JEI.23.1.013026","journal-title":"J Electron Imaging"},{"key":"44_CR19","doi-asserted-by":"publisher","first-page":"23607","DOI":"10.1007\/s11042-018-5623-3","volume":"77","author":"A Hmimid","year":"2018","unstructured":"Hmimid A, Sayyouri M, Qjidaa H (2018) Image classification using separable invariant moments of charlier-meixner and support vector machine. Multimedia Tools Appl 77:23607\u201323631. https:\/\/doi.org\/10.1007\/s11042-018-5623-3","journal-title":"Multimedia Tools Appl"},{"key":"44_CR20","doi-asserted-by":"crossref","unstructured":"Hou Q, Zhou D, Feng J (2021) Coordinate attention for efficient mobile network design. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp 13708\u201313717","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"44_CR21","doi-asserted-by":"publisher","unstructured":"Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) MobileNets: efficient convolutional neural networks for mobile vision applications. https:\/\/doi.org\/10.48550\/arXiv.1704.04861","DOI":"10.48550\/arXiv.1704.04861"},{"key":"44_CR22","doi-asserted-by":"crossref","unstructured":"Hu H, Wang W, Zhou W, Zhao W, Li H (2021) Model-aware gesture-to-gesture translation. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp 16423\u201316432","DOI":"10.1109\/CVPR46437.2021.01616"},{"key":"44_CR23","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1007\/978-3-319-46466-4_3","volume-title":"Computer vision \u2013 ECCV 2016","author":"E Insafutdinov","year":"2016","unstructured":"Insafutdinov E, Pishchulin L, Andres B, Andriluka M, Schiele B (2016) DeeperCut: a deeper, stronger, and faster multi-person pose estimation model. In: Leibe B, Matas J, Sebe N, Welling M (eds) Computer vision \u2013 ECCV 2016. Springer International Publishing, Cham, pp 34\u201350"},{"key":"44_CR24","doi-asserted-by":"crossref","unstructured":"Jahid T, Karmouni H, Hmimid A, Sayyouri M, Qjidaa H (2017) Image moments and reconstruction by krawtchouk via clenshaw\u2019s reccurence formula. In: 2017 International Conference on Electrical and Information Technologies (ICEIT). pp 1\u20137","DOI":"10.1109\/EITech.2017.8255265"},{"key":"44_CR25","doi-asserted-by":"publisher","first-page":"106345","DOI":"10.1016\/j.mejo.2024.106345","volume":"151","author":"M Jaiswal","year":"2024","unstructured":"Jaiswal M, Sharma V, Sharma A, Saini S, Tomar R (2024) Quantized CNN-based efficient hardware architecture for real-time hand gesture recognition. Microelectron J 151:106345. https:\/\/doi.org\/10.1016\/j.mejo.2024.106345","journal-title":"Microelectron J"},{"key":"44_CR26","doi-asserted-by":"publisher","first-page":"102061","DOI":"10.1016\/j.jksuci.2024.102061","volume":"36","author":"J Jiang","year":"2024","unstructured":"Jiang J, Xia N, Yu X (2024) A feature matching and compensation method based on importance weighting for occluded human pose estimation. J King Saud Univ Comput Inf Sci 36:102061. https:\/\/doi.org\/10.1016\/j.jksuci.2024.102061","journal-title":"J King Saud Univ Comput Inf Sci"},{"key":"44_CR27","doi-asserted-by":"publisher","first-page":"104772","DOI":"10.1016\/j.dsp.2024.104772","volume":"156","author":"B Jiang","year":"2025","unstructured":"Jiang B, Wu H, Xia Q, Li G, Xiao H, Zhao Y (2025) NKDFF-CNN: a convolutional neural network with narrow kernel and dual-view feature fusion for multitype gesture recognition based on sEMG. Digit Signal Process 156:104772. https:\/\/doi.org\/10.1016\/j.dsp.2024.104772","journal-title":"Digit Signal Process"},{"key":"44_CR28","doi-asserted-by":"publisher","unstructured":"Jocher G, Chaurasia A, Stoken A, Borovec J, NanoCode012 (2022) ultralytics\/yolov5: v7.0 - YOLOv5 SOTA realtime instance segmentation. Zenodo. https:\/\/doi.org\/10.5281\/zenodo.7347926","DOI":"10.5281\/zenodo.7347926"},{"key":"44_CR29","doi-asserted-by":"publisher","DOI":"10.1109\/WACV57701.2024.00451","author":"A Kapitanov","year":"2022","unstructured":"Kapitanov A, Kvanchiani K, Nagaev A, Kraynov R, Makhliarchuk A (2022) HaGRID - hand gesture recognition image dataset. IEEE. https:\/\/doi.org\/10.1109\/WACV57701.2024.00451","journal-title":"IEEE"},{"key":"44_CR30","doi-asserted-by":"publisher","first-page":"4015","DOI":"10.1007\/s00034-018-0755-2","volume":"37","author":"H Karmouni","year":"2018","unstructured":"Karmouni H, Hmimid A, Jahid T, Sayyouri M, Qjidaa H, Rezzouk A (2018) Fast and stable computation of the charlier moments and their inverses using digital filters and image block representation. Circuits Syst Signal Process 37:4015\u20134033. https:\/\/doi.org\/10.1007\/s00034-018-0755-2","journal-title":"Circuits Syst Signal Process"},{"key":"44_CR31","doi-asserted-by":"publisher","first-page":"31245","DOI":"10.1007\/s11042-019-07961-y","volume":"78","author":"H Karmouni","year":"2019","unstructured":"Karmouni H, Jahid T, Hmimid A, Sayyouri M, Qjidaa H (2019) Fast computation of inverse meixner moments transform using clenshaw\u2019s formula. Multimedia Tools Appl 78:31245\u201331265. https:\/\/doi.org\/10.1007\/s11042-019-07961-y","journal-title":"Multimedia Tools Appl"},{"key":"44_CR32","doi-asserted-by":"crossref","unstructured":"Karmouni H, Jahid T, Lakhili Z, Hmimid A, Sayyouri M, Qjidaa H, Rezzouk A (2017) Image reconstruction by krawtchouk moments via digital filter. In: 2017 Intelligent Systems and Computer Vision (ISCV). pp 1\u20137","DOI":"10.1109\/ISACV.2017.8054958"},{"key":"44_CR33","doi-asserted-by":"crossref","unstructured":"Karmouni H, Jahid T, Affar IE, Sayyouri M, Hmimid A, Qjidaa H, Rezzouk A (2017) Image analysis using separable krawtchouk-tchebichef\u2019s moments. In: 2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP). pp 1\u20135","DOI":"10.1109\/ATSIP.2017.8075581"},{"key":"44_CR34","doi-asserted-by":"publisher","first-page":"13498","DOI":"10.1109\/TITS.2021.3124981","volume":"23","author":"S Kreiss","year":"2022","unstructured":"Kreiss S, Bertoni L, Alahi A (2022) OpenPifPaf: composite fields for semantic keypoint detection and spatio-temporal association. IEEE Trans Intell Transp Syst 23:13498\u201313511. https:\/\/doi.org\/10.1109\/TITS.2021.3124981","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"44_CR35","doi-asserted-by":"crossref","unstructured":"Kreiss S, Bertoni L, Alahi A (2019) PifPaf: composite fields for human pose estimation. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp 11969\u201311978","DOI":"10.1109\/CVPR.2019.01225"},{"key":"44_CR36","doi-asserted-by":"publisher","first-page":"110536","DOI":"10.1016\/j.patcog.2024.110536","volume":"153","author":"Y Li","year":"2024","unstructured":"Li Y, Wei G, Desrosiers C, Zhou Y (2024) Decoupled and boosted learning for skeleton-based dynamic hand gesture recognition. Pattern Recognit 153:110536. https:\/\/doi.org\/10.1016\/j.patcog.2024.110536","journal-title":"Pattern Recognit"},{"key":"44_CR37","doi-asserted-by":"publisher","unstructured":"Li W, Wang Z, Yin B, Peng Q, Du Y, Xiao T, Yu G, Lu H, Wei Y, Sun J (2019) Rethinking on multi-stage networks for human pose estimation. https:\/\/doi.org\/10.48550\/arXiv.1901.00148","DOI":"10.48550\/arXiv.1901.00148"},{"key":"44_CR38","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/978-3-319-46448-0_2","volume-title":"Computer Vision \u2013 ECCV 2016","author":"W Liu","year":"2016","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) SSD: single shot MultiBox detector. In: Leibe B, Matas J, Sebe N, Welling M (eds) Computer Vision \u2013 ECCV 2016. Springer International Publishing, Cham, pp 21\u201337"},{"key":"44_CR39","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: hierarchical vision transformer using shifted windows. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV). pp 9992\u201310002","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"44_CR40","doi-asserted-by":"publisher","unstructured":"Luo Z, Wang Z, Huang Y, Wang L, Tan T, Zhou E (2021) Rethinking the Heatmap regression for bottom-up human pose estimation. 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 13259\u201313268.\u00a0https:\/\/doi.org\/10.1109\/CVPR46437.2021.01306","DOI":"10.1109\/CVPR46437.2021.01306"},{"key":"44_CR41","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1007\/978-3-030-01264-9_8","volume-title":"Computer vision \u2013 ECCV 2018","author":"N Ma","year":"2018","unstructured":"Ma N, Zhang X, Zheng H-T, Sun J (2018) ShuffleNet V2: practical guidelines for efficient CNN architecture design. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y (eds) Computer vision \u2013 ECCV 2018. Springer International Publishing, Cham, pp 122\u2013138"},{"key":"44_CR42","doi-asserted-by":"publisher","unstructured":"Mathieu B, Anas N, Thomas P, Robert P, Samir L (2024) Exploring the applications of natural language processing and language models for production, planning, and control activities of SMEs in industry 4.0: a systematic literature review. J Intell Manuf. https:\/\/doi.org\/10.1007\/s10845-024-02509-w","DOI":"10.1007\/s10845-024-02509-w"},{"key":"44_CR43","doi-asserted-by":"crossref","unstructured":"Moon G, Chang JY, Lee KM (2019) PoseFix: model-agnostic general human pose refinement network. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp 7765\u20137773","DOI":"10.1109\/CVPR.2019.00796"},{"key":"44_CR44","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1016\/j.jksuci.2014.03.023","volume":"27","author":"MA Muhanna","year":"2015","unstructured":"Muhanna MA (2015) Virtual reality and the CAVE: taxonomy, interaction challenges and research directions. J King Saud Univ Comput Inf Sci 27:344\u2013361. https:\/\/doi.org\/10.1016\/j.jksuci.2014.03.023","journal-title":"J King Saud Univ Comput Inf Sci"},{"key":"44_CR45","doi-asserted-by":"publisher","unstructured":"Odenigbo IP, Alslaity A, Chan G, Orji R (2024) AR Dancee: an augmented reality-based mobile persuasive intervention for promoting physical activity through dancing. International J Human\u2013Comput Interact 0:1\u201321. https:\/\/doi.org\/10.1080\/10447318.2024.2384136","DOI":"10.1080\/10447318.2024.2384136"},{"key":"44_CR46","doi-asserted-by":"publisher","first-page":"3711","DOI":"10.1109\/CVPR.2017.395","volume":"2017","author":"G Papandreou","year":"2017","unstructured":"Papandreou G, Zhu T, Kanazawa N, Toshev A, Tompson J, Bregler C, Murphy K (2017) Towards accurate multi-person pose estimation in the wild. IEEE Conf Comput vis Pattern Recognit (CVPR) 2017:3711\u20133719. https:\/\/doi.org\/10.1109\/CVPR.2017.395","journal-title":"IEEE Conf Comput vis Pattern Recognit (CVPR)"},{"key":"44_CR47","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1007\/978-3-030-01264-9_17","volume-title":"Computer Vision \u2013 ECCV 2018","author":"G Papandreou","year":"2018","unstructured":"Papandreou G, Zhu T, Chen L-C, Gidaris S, Tompson J, Murphy K (2018) PersonLab: person pose estimation and instance segmentation with a bottom-up, part-based, geometric embedding model. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y (eds) Computer Vision \u2013 ECCV 2018. Springer International Publishing, Cham, pp 282\u2013299"},{"key":"44_CR48","doi-asserted-by":"crossref","unstructured":"Pishchulin L, Insafutdinov E, Tang S, Andres B, Andriluka M, Gehler P, Schiele B (2016) DeepCut: joint subset partition and labeling for multi person pose estimation. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp 4929\u20134937","DOI":"10.1109\/CVPR.2016.533"},{"key":"44_CR49","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39:1137\u20131149. https:\/\/doi.org\/10.1109\/TPAMI.2016.2577031","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"44_CR50","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-024-03394-3","author":"S Saboo","year":"2024","unstructured":"Saboo S, Singha J (2024) Semantic hand gesture integration system using self-co-articulation and movement epenthesis detection. Visual Comput. https:\/\/doi.org\/10.1007\/s00371-024-03394-3","journal-title":"Visual Comput"},{"key":"44_CR51","doi-asserted-by":"publisher","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C (2018) MobileNetV2: inverted residuals and linear bottlenecks. 2018 IEEE\/CVF conference on computer vision and pattern recognition 4510\u20134520.\u00a0https:\/\/doi.org\/10.1109\/CVPR.2018.00474","DOI":"10.1109\/CVPR.2018.00474"},{"key":"44_CR52","doi-asserted-by":"publisher","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. Comput Sci. https:\/\/doi.org\/10.48550\/arXiv.1409.1556","DOI":"10.48550\/arXiv.1409.1556"},{"key":"44_CR53","doi-asserted-by":"publisher","unstructured":"Tan M, Le QV (2019) EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. https:\/\/doi.org\/10.48550\/arXiv.1905.11946","DOI":"10.48550\/arXiv.1905.11946"},{"key":"44_CR54","doi-asserted-by":"crossref","unstructured":"Tan M, Pang R, Le QV (2020) EfficientDet: scalable and efficient object detection. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp 10778\u201310787","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"44_CR55","doi-asserted-by":"publisher","first-page":"6171","DOI":"10.1007\/s00371-023-03160-x","volume":"40","author":"R Tripathi","year":"2024","unstructured":"Tripathi R, Verma B (2024) Survey on vision-based dynamic hand gesture recognition. Visual Comput 40:6171\u20136199. https:\/\/doi.org\/10.1007\/s00371-023-03160-x","journal-title":"Visual Comput"},{"key":"44_CR56","doi-asserted-by":"crossref","unstructured":"Varghese R, Sambath M (2024) YOLOv8: a novel object detection algorithm with enhanced performance and robustness. In: 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS). pp 1\u20136","DOI":"10.1109\/ADICS58448.2024.10533619"},{"key":"44_CR57","doi-asserted-by":"crossref","unstructured":"Wang C-Y, Mark Liao H-Y, Wu Y-H, Chen P-Y, Hsieh J-W, Yeh I-H (2020) CSPNet: a new backbone that can enhance learning capability of CNN. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). pp 1571\u20131580","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"44_CR58","doi-asserted-by":"crossref","unstructured":"Wang C-Y, Bochkovskiy A, Liao H-YM (2023) YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp 7464\u20137475","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"44_CR59","doi-asserted-by":"publisher","unstructured":"Xu B, Wang N, Chen T, Li M (2015) Empirical evaluation of rectified activations in convolutional network. Computer ence. https:\/\/doi.org\/10.48550\/arXiv.1505.00853","DOI":"10.48550\/arXiv.1505.00853"},{"key":"44_CR60","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1016\/j.procs.2019.01.054","volume":"148","author":"M Yamni","year":"2019","unstructured":"Yamni M, Daoui A, Ogri OE, Karmouni H, Sayyouri M, Qjidaa H (2019) Influence of krawtchouk and charlier moment\u2019s parameters on image reconstruction and classification. Procedia Comput Sci 148:418\u2013427. https:\/\/doi.org\/10.1016\/j.procs.2019.01.054","journal-title":"Procedia Comput Sci"},{"key":"44_CR61","doi-asserted-by":"publisher","first-page":"102061","DOI":"10.1016\/j.aei.2023.102061","volume":"57","author":"W Zhu","year":"2023","unstructured":"Zhu W, Zhang H, Zhang C, Zhu X, Guan Z, Jia J (2023) Surface defect detection and classification of steel using an efficient Swin transformer. Adv Eng Inform 57:102061. https:\/\/doi.org\/10.1016\/j.aei.2023.102061","journal-title":"Adv Eng Inform"}],"container-title":["Journal of King Saud University Computer and Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00044-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44443-025-00044-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00044-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T13:04:10Z","timestamp":1751893450000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44443-025-00044-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,20]]},"references-count":61,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["44"],"URL":"https:\/\/doi.org\/10.1007\/s44443-025-00044-z","relation":{},"ISSN":["1319-1578","2213-1248"],"issn-type":[{"value":"1319-1578","type":"print"},{"value":"2213-1248","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,20]]},"assertion":[{"value":"7 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 May 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"No potential conflict of interest was reported by the author(s).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"44"}}