{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T18:28:09Z","timestamp":1772303289268,"version":"3.50.1"},"reference-count":27,"publisher":"World Scientific Pub Co Pte Ltd","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2025,2]]},"abstract":"<jats:p> The process of utilizing Artificial Intelligence (AI) to identify and label human behaviors from unprocessed activity data gathered from various sources is known as Human Activity Recognition (HAR). Because of its potential applications across multiple areas, computer vision faces a significant challenge in recognizing human actions and the accompanying interactions with objects and the environment. Investigating the temporal and geographical characteristics of the skeleton sequence is essential for this endeavor, according to recent studies. However, efficiently extracting discriminative temporal and spatial information remains a difficult task. This work proposes a novel Human Action Recognition Model exploiting improved Graph Convolutional Network (GCN)-based pose estimation with a Hybrid Classifier (IGCN-HC). The phases carried out in this model are pre-processing, pose estimation, feature extraction, and activity recognition. Initially, the input video will be pre-processed and a frame from the input video stream will be generated. Subsequently, human pose estimation exploiting improved GCN will be accomplished. Further, human skeletal joints\u2019 coordinates in two- or three-dimensional spaces are determined via human pose estimation. Then, Shape Local Binary Texture (SLBT) and an improved hierarchy of skeleton features have been used to detect the variance in different activities. In the last phase, a hybrid classification model with the combination of Deep Maxout and customized CNN has been proposed for the recognition phase. The model utilizes two inputs pose estimation results (skeleton) and the extracted features for training purposes. Finally, the proposed trained model is evaluated for recognition on different test inputs and contrasted with the existing techniques. <\/jats:p>","DOI":"10.1142\/s0218001424570167","type":"journal-article","created":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T09:31:46Z","timestamp":1734687106000},"source":"Crossref","is-referenced-by-count":2,"title":["Automated Human Action Recognition with Improved Graph Convolutional Network-based Pose Estimation"],"prefix":"10.1142","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8930-7644","authenticated-orcid":false,"given":"Amit","family":"Baghel","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, School of Studies of Engineering and Technology, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur 495009, Chhattisgarh, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2928-998X","authenticated-orcid":false,"given":"Alok Kumar Singh","family":"Kushwaha","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, School of Studies of Engineering and Technology, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur 495009, Chhattisgarh, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8527-1162","authenticated-orcid":false,"given":"Roshan","family":"Singh","sequence":"additional","affiliation":[{"name":"Centre for Computing and Information Services, IIT, BHU, Varanasi 221005, Uttar Pradesh, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2025,2,24]]},"reference":[{"key":"S0218001424570167BIB001","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-022-07582-y"},{"key":"S0218001424570167BIB002","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-020-09593-z"},{"key":"S0218001424570167BIB003","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2997962"},{"key":"S0218001424570167BIB004","doi-asserted-by":"publisher","DOI":"10.1007\/s11390-020-0405-6"},{"key":"S0218001424570167BIB005","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-021-03827-z"},{"key":"S0218001424570167BIB006","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-04712-1"},{"key":"S0218001424570167BIB007","doi-asserted-by":"publisher","DOI":"10.1007\/s11071-020-05468-y"},{"key":"S0218001424570167BIB008","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-017-5496-x"},{"key":"S0218001424570167BIB009","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-021-06149-7"},{"key":"S0218001424570167BIB010","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-019-7365-2"},{"key":"S0218001424570167BIB011","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-06239-5"},{"key":"S0218001424570167BIB012","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-021-10866-4"},{"key":"S0218001424570167BIB013","doi-asserted-by":"publisher","DOI":"10.1007\/s42452-021-04528-1"},{"key":"S0218001424570167BIB014","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmsy.2020.04.018"},{"key":"S0218001424570167BIB015","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106513"},{"key":"S0218001424570167BIB016","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108170"},{"key":"S0218001424570167BIB017","doi-asserted-by":"publisher","DOI":"10.1016\/j.cogsys.2019.12.004"},{"key":"S0218001424570167BIB018","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2021.04.002"},{"key":"S0218001424570167BIB019","first-page":"196","volume":"23","author":"Agahian S.","year":"2020","journal-title":"Eng. 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