Real-time 6DoF full-range markerless head pose estimation
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Algabri, Redhwan | - |
dc.contributor.author | Shin, Hyunsoo | - |
dc.contributor.author | Lee, Sungon | - |
dc.date.accessioned | 2023-11-24T02:30:03Z | - |
dc.date.available | 2023-11-24T02:30:03Z | - |
dc.date.issued | 2024-04 | - |
dc.identifier.issn | 0957-4174 | - |
dc.identifier.issn | 1873-6793 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/115636 | - |
dc.description.abstract | Head 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.extent | 13 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Elsevier Ltd | - |
dc.title | Real-time 6DoF full-range markerless head pose estimation | - |
dc.title.alternative | Real-time 6DoF full-range markerless head pose estimation[Formula presented] | - |
dc.type | Article | - |
dc.publisher.location | 영국 | - |
dc.identifier.doi | 10.1016/j.eswa.2023.122293 | - |
dc.identifier.scopusid | 2-s2.0-85176277005 | - |
dc.identifier.wosid | 001111967200001 | - |
dc.identifier.bibliographicCitation | Expert Systems with Applications, v.239, pp 1 - 13 | - |
dc.citation.title | Expert Systems with Applications | - |
dc.citation.volume | 239 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 13 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Operations Research & Management Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Operations Research & Management Science | - |
dc.subject.keywordAuthor | 6DoF poses | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Full-range angles | - |
dc.subject.keywordAuthor | Head pose estimation | - |
dc.subject.keywordAuthor | Landmark-free | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S0957417423027951?via%3Dihub | - |
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