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Real-time 6DoF full-range markerless head pose estimation

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dc.contributor.authorAlgabri, Redhwan-
dc.contributor.authorShin, Hyunsoo-
dc.contributor.authorLee, Sungon-
dc.date.accessioned2023-11-24T02:30:03Z-
dc.date.available2023-11-24T02:30:03Z-
dc.date.issued2024-04-
dc.identifier.issn0957-4174-
dc.identifier.issn1873-6793-
dc.identifier.urihttps://scholarworks.bwise.kr/erica/handle/2021.sw.erica/115636-
dc.description.abstractHead pose estimation is a fundamental function for several applications in human–computer interactions. Accurate six degrees of freedom head pose estimation (6DoF-HPE) with full-range angles make up most of these applications, which require sequential images of the human head as input. Most existing head pose estimation methods focus on a three degrees of freedom (3DoF) frontal head, which restricts their applications in real-world scenarios. This study presents a framework designed to estimate a head pose without landmark localization. The novelty of our framework is to estimate the 6DoF head poses under full-range angles in real-time. The proposed framework leverages deep neural networks to detect human heads and predict their angles using single shot multibox detector (SSD) and RepVGG-b1g4 backbone, respectively. This work uses red, green, blue, and depth (RGB-D) data to estimate the rotational and translational components relative to the camera pose. The proposed framework employs a continuous representation to predict the angles and a multi-loss approach to update the loss functions for the training strategy. The regression function combines the geodesic loss with the mean squared error. The ground-truth labels were extracted from the public dataset Carnegie Mellon university (CMU) Panoptic for full head angles. This study provides a comprehensive comparison with state-of-the-art methods using public benchmark datasets. Experiments demonstrate that the proposed method achieves or outperforms state-of-the-art methods. The code and datasets are available at: (https://github.com/Redhwan-A/6DoFHPE).-
dc.format.extent13-
dc.language영어-
dc.language.isoENG-
dc.publisherElsevier Ltd-
dc.titleReal-time 6DoF full-range markerless head pose estimation-
dc.title.alternativeReal-time 6DoF full-range markerless head pose estimation[Formula presented]-
dc.typeArticle-
dc.publisher.location영국-
dc.identifier.doi10.1016/j.eswa.2023.122293-
dc.identifier.scopusid2-s2.0-85176277005-
dc.identifier.wosid001111967200001-
dc.identifier.bibliographicCitationExpert Systems with Applications, v.239, pp 1 - 13-
dc.citation.titleExpert Systems with Applications-
dc.citation.volume239-
dc.citation.startPage1-
dc.citation.endPage13-
dc.type.docTypeArticle-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalResearchAreaOperations Research & Management Science-
dc.relation.journalWebOfScienceCategoryComputer Science, Artificial Intelligence-
dc.relation.journalWebOfScienceCategoryEngineering, Electrical & Electronic-
dc.relation.journalWebOfScienceCategoryOperations Research & Management Science-
dc.subject.keywordAuthor6DoF poses-
dc.subject.keywordAuthorDeep learning-
dc.subject.keywordAuthorFull-range angles-
dc.subject.keywordAuthorHead pose estimation-
dc.subject.keywordAuthorLandmark-free-
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S0957417423027951?via%3Dihub-
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