期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 239, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2023.122293
关键词
Head pose estimation; Full-range angles; 6DoF poses; Landmark-free; Deep learning
This study presents a novel framework for estimating head pose in real-time without the need for landmark localization. The proposed method utilizes deep neural networks and multi-loss approach to predict head poses by analyzing RGB-D data.
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).
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据