Real-time 6DoF full-range markerless head pose estimation[Formula presented]
- Title
- Real-time 6DoF full-range markerless head pose estimation[Formula presented]
- Author
- 이성온
- Keywords
- 6DoF poses; Deep learning; Full-range angles; Head pose estimation; Landmark-free
- Issue Date
- 2024-04
- Publisher
- Elsevier Ltd
- Citation
- Expert Systems with Applications, v. 239, article no. 122293, Page. 1.0-13.0
- 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). © 2023 The Author(s)
- URI
- https://www.sciencedirect.com/science/article/pii/S0957417423027951?via%3Dihubhttps://repository.hanyang.ac.kr/handle/20.500.11754/187508
- ISSN
- 0957-4174;1873-6793
- DOI
- 10.1016/j.eswa.2023.122293
- Appears in Collections:
- COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > ROBOT ENGINEERING(로봇공학과) > Articles
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- 110298_이성온.pdfDownload
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