{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T21:11:35Z","timestamp":1777583495059,"version":"3.51.4"},"reference-count":63,"publisher":"Walter de Gruyter GmbH","issue":"4","license":[{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/3.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Two-dimensional human pose estimation has been widely applied in real-world applications such as sports analysis, medical fall detection, human-robot interaction, with many positive results obtained utilizing Convolutional Neural Networks (CNNs). Li et al. at CVPR 2020 proposed a study in which they achieved high accuracy in estimating 2D keypoints estimation\/2D human pose estimation. However, the study performed estimation only on the cropped human image data. In this research, we propose a method for automatically detecting and estimating human poses in photos using a combination of YOLOv5 + CC (Contextual Constraints) and HRNet. Our approach inherits the speed of the YOLOv5 for detecting humans and the efficiency of the HRNet for estimating 2D keypoints\/2D human pose on the images. We also performed human marking on the images by bounding boxes of the Human 3.6M dataset (Protocol #1) for human detection evaluation. Our approach obtained high detection results in the image and the processing time is 55 FPS on the Human 3.6M dataset (Protocol #1). The mean error distance is 5.14 pixels on the full size of the image (1000 \u00d7 1002). In particular, the average results of 2D human pose estimation\/2D keypoints estimation are 94.8% of PCK and 99.2% of PDJ@0.4 (head joint). The results are available.<\/jats:p>","DOI":"10.2478\/jaiscr-2022-0019","type":"journal-article","created":{"date-parts":[[2022,10,29]],"date-time":"2022-10-29T13:20:09Z","timestamp":1667049609000},"page":"281-298","source":"Crossref","is-referenced-by-count":21,"title":["Combined YOLOv5 and HRNet for High Accuracy 2D Keypoint and Human Pose Estimation"],"prefix":"10.2478","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7292-3611","authenticated-orcid":false,"given":"Hung-Cuong","family":"Nguyen","sequence":"first","affiliation":[{"name":"Faculty of Engineering Technology , Hung Vuong University , Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8809-0201","authenticated-orcid":false,"given":"Thi-Hao","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Faculty of Engineering Technology , Hung Vuong University , Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9592-262X","authenticated-orcid":false,"given":"Jakub","family":"Nowak","sequence":"additional","affiliation":[{"name":"Czestochowa University of Technology , Czestochowa , Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1572-3426","authenticated-orcid":false,"given":"Aleksander","family":"Byrski","sequence":"additional","affiliation":[{"name":"AGH University of Science and Technology , Institute of Computer Science , Krak\u00f3w , Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2562-236X","authenticated-orcid":false,"given":"Agnieszka","family":"Siwocha","sequence":"additional","affiliation":[{"name":"University of Social Sciences , Institute of Information Technologies , 9 Sienkiewicza Street , \u0141\u00f3d\u017a , Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4302-0581","authenticated-orcid":false,"given":"Van-Hung","family":"Le","sequence":"additional","affiliation":[{"name":"Tan Trao University , Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2022,10,29]]},"reference":[{"key":"2026042814152024991_j_jaiscr-2022-0019_ref_001","unstructured":"[1] Ssd mobilenet v1 architecture (2018). 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