{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T05:49:04Z","timestamp":1772689744477,"version":"3.50.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,6,28]],"date-time":"2025-06-28T00:00:00Z","timestamp":1751068800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,28]],"date-time":"2025-06-28T00:00:00Z","timestamp":1751068800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Ho Chi Minh City University of Education Foun- dation for Science and Technology","award":["CS.2024.19"],"award-info":[{"award-number":["CS.2024.19"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-025-04118-7","type":"journal-article","created":{"date-parts":[[2025,6,28]],"date-time":"2025-06-28T07:59:57Z","timestamp":1751097597000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Advancing Violence Detection with Graph-Based Skeleton Motion Analysis"],"prefix":"10.1007","volume":"6","author":[{"given":"Nha","family":"Tran","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6770-0528","authenticated-orcid":false,"given":"Hung","family":"Nguyen","sequence":"additional","affiliation":[]},{"given":"Dat","family":"Ly","sequence":"additional","affiliation":[]},{"given":"Khanh","family":"Ngo","sequence":"additional","affiliation":[]},{"given":"Hien D.","family":"Nguyen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,28]]},"reference":[{"key":"4118_CR1","unstructured":"Violence against children. Retrieved 25 March 2024, from https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/violence-against-children 2024."},{"key":"4118_CR2","doi-asserted-by":"crossref","unstructured":"Tran N, Nguyen H, Ly D, Nguyen HD. Violence Detection Using Skeleton Data with Graph Convolutional Networks,\u201d in International Conference on Intelligent Systems and Data Science, Springer Nature Singapore, 2024. pp. 86\u201397.","DOI":"10.1007\/978-981-97-9616-8_7"},{"key":"4118_CR3","doi-asserted-by":"crossref","unstructured":"Kaur G, Singh S. Violence detection in videos using deep learning: A survey,\u201d in Advances in Information Communication Technology and Computing: Proceedings of AICTC 2021, 2022. pp. 165\u2013173.","DOI":"10.1007\/978-981-19-0619-0_15"},{"key":"4118_CR4","doi-asserted-by":"publisher","first-page":"10400","DOI":"10.1002\/int.22537","volume":"37","author":"F Ullah","year":"2021","unstructured":"Ullah F, et al. An intelligent system for complex violence pattern analysis and detection. Int J Intell Syst. 2021;37:10400\u201322.","journal-title":"Int J Intell Syst"},{"key":"4118_CR5","doi-asserted-by":"publisher","first-page":"696","DOI":"10.1109\/TCSVT.2016.2589858","volume":"27","author":"T Zhang","year":"2017","unstructured":"Zhang T, Jia W, He X, Yang J. Discriminative dictionary learning with motion weber local descriptor for violence detection. IEEE Trans Circuits Syst Video Technol. 2017;27:696\u2013709.","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"4118_CR6","first-page":"2","volume":"5","author":"LR Medsker","year":"2001","unstructured":"Medsker LR, Jain L, et al. Recurrent neural networks Design and Applications. 2001;5:2.","journal-title":"Recurrent neural networks. Design and Applications"},{"key":"4118_CR7","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.patrec.2020.11.018","volume":"142","author":"P Wang","year":"2021","unstructured":"Wang P, Wang P, Fan E. Violence detection and face recognition based on deep learning. Pattern Recogn Lett. 2021;142:20\u20134.","journal-title":"Pattern Recogn Lett"},{"key":"4118_CR8","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","volume":"37","author":"K He","year":"2015","unstructured":"He K, Zhang X, Ren S, Sun J. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell. 2015;37:1904\u201316.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4118_CR9","doi-asserted-by":"publisher","first-page":"2472","DOI":"10.3390\/s19112472","volume":"19","author":"F Ullah","year":"2019","unstructured":"Ullah F, Ullah A, Muhammad K, Haq I, Baik S. Violence detection using spatiotemporal features with 3d convolutional neural network. Sensors. 2019;19:2472.","journal-title":"Sensors"},{"key":"4118_CR10","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s11263-005-1838-7","volume":"64","author":"I Laptev","year":"2005","unstructured":"Laptev I. On space-time interest points. Int J Comput Vision. 2005;64:107\u201323.","journal-title":"Int J Comput Vision"},{"key":"4118_CR11","doi-asserted-by":"publisher","first-page":"1515","DOI":"10.1007\/s00371-023-02865-3","volume":"40","author":"TZ Ehsan","year":"2024","unstructured":"Ehsan TZ, Nahvi M, Mohtavipour SM. An accurate violence detection framework using unsupervised spatial-temporal action translation network. Vis Comput. 2024;40:1515\u201335.","journal-title":"Vis Comput"},{"key":"4118_CR12","doi-asserted-by":"publisher","first-page":"315","DOI":"10.32604\/cmc.2022.024566","volume":"72","author":"B Omarov","year":"2022","unstructured":"Omarov B, Narynov S, Zhumanov Z, Gumar A, Khassanova M. A skeleton-based approach for campus violence detection. Computers, Materials & Continua. 2022;72:315\u201331.","journal-title":"Computers, Materials & Continua"},{"key":"4118_CR13","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1109\/TAI.2021.3076974","volume":"2","author":"T Ahmad","year":"2021","unstructured":"Ahmad T, et al. Graph convolutional neural network for human action recognition: A comprehensive survey. IEEE Transactions on Artificial Intelligence. 2021;2:128\u201345.","journal-title":"IEEE Transactions on Artificial Intelligence"},{"key":"4118_CR14","doi-asserted-by":"publisher","first-page":"3912","DOI":"10.1109\/TETCI.2024.3378331","volume":"8","author":"H Zou","year":"2024","unstructured":"Zou H, et al. Gt-whar: A generic graph-based temporal framework for wearable human activity recognition with multiple sensors. IEEE Transactions on Emerging Topics in Computational Intelligence. 2024;8:3912\u201324.","journal-title":"IEEE Transactions on Emerging Topics in Computational Intelligence"},{"key":"4118_CR15","doi-asserted-by":"publisher","first-page":"2919","DOI":"10.1007\/s10878-021-00815-0","volume":"44","author":"QM Tran","year":"2022","unstructured":"Tran QM, Nguyen H, Huynh T, et al. Measuring the influence and amplification of users on social network with unsupervised behaviors learning and efficient interaction-based knowledge graph. J Comb Optim. 2022;44:2919\u201345.","journal-title":"J Comb Optim"},{"key":"4118_CR16","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1080\/24751839.2024.2367387","volume":"9","author":"V-D Hoang","year":"2025","unstructured":"Hoang V-D, et al. Powering ai-driven car damage identification based on vehide dataset. Journal of Information and Telecommunication. 2025;9:24\u201343.","journal-title":"Journal of Information and Telecommunication"},{"key":"4118_CR17","doi-asserted-by":"crossref","unstructured":"Tran NN, Nguyen HD, Huynh NT, Tran NP, Nguyen LV. Segmentation on Chest CT Imaging in COVID-19 Based on the Improvement Attention U-Net Model. In: New Trends in Intelligent Software Methodologies, Tools and Techniques (SoMeT 2022), Kitakyushu, Japan, 2022. pp. 596\u2013606.","DOI":"10.3233\/FAIA220288"},{"key":"4118_CR18","doi-asserted-by":"crossref","unstructured":"Cheng M, Cai K, Li M. RWF-2000: An open large scale video database for violence detection. In: 25th International Conference on Pattern Recognit. (ICPR), Milan, Italy, 2021. pp. 4183\u20134190.","DOI":"10.1109\/ICPR48806.2021.9412502"},{"key":"4118_CR19","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0120448","volume":"10","author":"IS Gracia","year":"2015","unstructured":"Gracia IS, Suarez OD, Garcia GB, Kim T-K. Fast fight detection. PLoS ONE. 2015;10: e0120448.","journal-title":"PLoS ONE"},{"key":"4118_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3561971","volume":"55","author":"FU Ullah","year":"2023","unstructured":"Ullah FU, et al. A comprehensive review on vision-based violence detection in surveillance videos. ACM Comput Surv. 2023;55:1\u201344.","journal-title":"ACM Comput Surv"},{"key":"4118_CR21","doi-asserted-by":"publisher","unstructured":"Soliman MM, Kamal MH, Nashed MA, Mostafa YM, Chawky BS, Khattab D. Violence recognition from videos using deep learning techniques. In: 2019 ninth international conference on intelligent computing and information systems (ICICIS), Cairo, Egypt, 2019. pp. 1\u20136. https:\/\/doi.org\/10.1109\/ICICIS46948.2019.9014714.","DOI":"10.1109\/ICICIS46948.2019.9014714"},{"key":"4118_CR22","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1016\/j.procs.2020.06.030","volume":"173","author":"V Singh","year":"2020","unstructured":"Singh V, Singh S, Gupta P. Real-time anomaly recognition through cctv using neural networks. Procedia Computer Science. 2020;173:254\u201363.","journal-title":"Procedia Computer Science"},{"key":"4118_CR23","doi-asserted-by":"publisher","first-page":"76270","DOI":"10.1109\/ACCESS.2021.3083273","volume":"9","author":"MS Kang","year":"2021","unstructured":"Kang MS, Park RH, Park HM. Efficient spatio-temporal modeling methods for real-time violence recognition. IEEE Access. 2021;9:76270\u201385.","journal-title":"IEEE Access"},{"key":"4118_CR24","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1007\/s42979-024-03126-3","volume":"5","author":"L Ciampi","year":"2024","unstructured":"Ciampi L, Santiago C, Falchi F, et al. In the wild video violence detection: An unsupervised domain adaptation approach. SN COMPUT SCI. 2024;5:834.","journal-title":"SN COMPUT. SCI."},{"key":"4118_CR25","doi-asserted-by":"publisher","unstructured":"Narynov S, Zhumanov Z, Gumar A, Khassanova M, Omarov B. Physical violence detection in video streaming using partitioned skeleton analysis. In: 21st International Conference on Control, Automation and Systems (ICCAS), Jeju, South Korea, 2021. pp. 1\u20136. https:\/\/doi.org\/10.23919\/ICCAS52745.2021.9649827.","DOI":"10.23919\/ICCAS52745.2021.9649827"},{"key":"4118_CR26","unstructured":"Su Y, Lin G, Wu Q. Improving video violence recognition with human interaction learning on 3d skeleton point clouds. arXiv preprint arXiv:2308.13866 2023."},{"key":"4118_CR27","doi-asserted-by":"crossref","unstructured":"Sudhakaran S, Lanz O. Learning to detect violent videos using convolutional long short-term memory. In: 14th IEEE international conference on advanced video and signal based surveillance (AVSS), Lecce, Italy, 2017. pp. 1\u20136.","DOI":"10.1109\/AVSS.2017.8078468"},{"key":"4118_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2023.103739","volume":"233","author":"G Garcia-Cobo","year":"2023","unstructured":"Garcia-Cobo G, SanMiguel JC. Human skeletons and change detection for efficient violence detection in surveillance videos. Comput Vis Image Underst. 2023;233: 103739.","journal-title":"Comput Vis Image Underst"},{"key":"4118_CR29","doi-asserted-by":"crossref","unstructured":"Yan S, Xiong Y, Lin D. Spatial temporal graph convolutional networks for skeleton-based action recognition 2018.","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"4118_CR30","doi-asserted-by":"crossref","unstructured":"Shi L, Zhang Y, Cheng J, Lu H. Two-stream adaptive graph convolutional networks for skeleton-based action recognition 2019.","DOI":"10.1109\/CVPR.2019.01230"},{"key":"4118_CR31","doi-asserted-by":"publisher","first-page":"2323337","DOI":"10.1155\/2024\/2323337","volume":"2024","author":"NF Janbi","year":"2024","unstructured":"Janbi NF, Ghaseb MA, Almazroi AA. Ests-gcn: An ensemble spatial-temporal skeleton-based graph convolutional networks for violence detection. Int J Intell Syst. 2024;2024:2323337.","journal-title":"Int J Intell Syst"},{"key":"4118_CR32","doi-asserted-by":"crossref","unstructured":"Azzam R, Kong F, Taha T, Zweiri Y. Pose-graph neural network classifier for global optimality prediction in 2d slam. IEEE Access 2021;1\u20131.","DOI":"10.1109\/ACCESS.2021.3084599"},{"key":"4118_CR33","unstructured":"Zhu J, Song Y, Zhao L, Li H. A3t-gcn: Attention temporal graph convolutional network for traffic forecasting. arXiv preprint arXiv: 2006.11583 2020."},{"key":"4118_CR34","unstructured":"Nievas EB, Suarez OD, Garcia GB, Sukthankar R. Hockey fight detection dataset. In: Computer Analysis of Images and Patterns (CAIP), Seville, Spain, Aug. 29\u201331, 2011. pp. 332\u2013339."},{"key":"4118_CR35","doi-asserted-by":"crossref","unstructured":"Bermejo Nievas E, Deniz Suarez O, Bueno Garc\u00eda G, Sukthankar R. Violence detection in video using computer vision techniques. In: Computer Analysis of Images and Patterns: 14th International Conference (CAIP 2011), Seville, Spain, Aug. 29\u201331, 2011, pp. 332\u2013339.","DOI":"10.1007\/978-3-642-23678-5_39"},{"key":"4118_CR36","unstructured":"Ultralytics yolov8 docs. Retrieved January 10, 2025, from https:\/\/docs.ultralytics.com\/."},{"key":"4118_CR37","unstructured":"Yolov8 vs yolov5: A detailed comparison. Retrieved May 20, 2025, from https:\/\/docs.ultralytics.com\/compare\/yolov8-vs-yolov5\/."},{"key":"4118_CR38","unstructured":"Cao Z, Hidalgo Martinez G, Simon T, Wei S, Sheikh YA. Openpose: Realtime multi-person 2d pose estimation using part affinity fields. IEEE Transactions on Pattern Analysis and Machine Intelligence 2019."},{"key":"4118_CR39","unstructured":"Ultralytics. Yolo-pose: Real-time pose estimation (2024). Retrieved May 20, 2025, from https:\/\/docs.ultralytics.com\/vision\/yolo-pose\/."},{"key":"4118_CR40","unstructured":"Deniz O, Serrano I, Bueno G, Kim TK. Fast violence detection in video. In: 2014 international conference on computer vision theory and applications (VISAPP), vol. 2, Lisbon, Portugal, 2014. pp. 478\u2013485."}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-04118-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-025-04118-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-04118-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,28]],"date-time":"2025-06-28T08:00:10Z","timestamp":1751097610000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-025-04118-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,28]]},"references-count":40,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["4118"],"URL":"https:\/\/doi.org\/10.1007\/s42979-025-04118-7","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,28]]},"assertion":[{"value":"2 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 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":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"595"}}