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However, monitoring multiple video feeds with multiple targets is challenging for human operators. Therefore, automatic and smart surveillance systems have been introduced to support or replace traditional surveillance systems and build safer communities. Advancements in artificial intelligence techniques, particularly in the field of computer vision, have boosted this area of research. Most existing works have focused on image\u2010based (RGB\u2010based) machine learning and deep learning algorithms for detecting anomalous and violent events. In this study, we propose a unique Ensemble Spatial\u2013Temporal Skeleton\u2010Based Graph Convolutional Networks (ESTS\u2010GCNs) model for violence detection that automatically uses spatial and temporal data to detect violence in surveillance videos. Skeleton\u2010based algorithms are less sensitive to pixel\u2010based noise and background interference, making them excellent candidates for activity and anomaly detection. Our proposed ensemble\u2010based architecture utilizes Graph Convolutional Networks (GCNs) and comprises multiple spatial and temporal modules. Three different spatial pipelines are exploited: channel\u2010wise topologies, self\u2010attention mechanism, and graph attention networks. The models were trained and evaluated using two skeleton\u2010based datasets introduced by us: Skeleton\u2010based Real\u2010Life Violence Situations (RLVS) and NTU\u2010Violence (NTU\u2010V). Our model achieved a maximum accuracy of around 93% and outperformed existing models by more than 10%.<\/jats:p>","DOI":"10.1155\/2024\/2323337","type":"journal-article","created":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T15:20:32Z","timestamp":1729869632000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["ESTS\u2010GCN: An Ensemble Spatial\u2013Temporal Skeleton\u2010Based Graph Convolutional Networks for Violence Detection"],"prefix":"10.1155","volume":"2024","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7330-1566","authenticated-orcid":false,"given":"Nourah Fahad","family":"Janbi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1682-7819","authenticated-orcid":false,"given":"Musrea Abdo","family":"Ghaseb","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7181-2100","authenticated-orcid":false,"given":"Abdulwahab Ali","family":"Almazroi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,10,25]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2021.103116"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.3390\/s20205796"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.3390\/s22051854"},{"key":"e_1_2_10_4_2","doi-asserted-by":"crossref","unstructured":"JanbiN. 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