3D Ego-Pose Lift-Up Robustness Study for Fisheye Camera Perturbations
3D Ego-Pose Lift-Up Robustness Study for Fisheye Camera Perturbations
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DOI：
10.5220/0011661000003417
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Teppei Miura;Shinji Sako;Tsutomu Kimura
中科院分区：
文献类型：
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作者：
Teppei Miura;Shinji Sako;Tsutomu Kimura
随着卷积神经网络和合成数据生成的进步，已经开发了来自安装的鱼眼相机的3D以自我为中心的人类姿势估计。相机捕获不同的图像，这些图像受到光学特性、安装位置和身体运动引起的相机扰动的影响。因此，数据收集和模型训练是从安装的鱼眼相机估计3D自我姿态的主要挑战。过去的工作提出了合成数据生成和两步估计模型，包括2D人体姿态估计和随后的3D提升，以克服的任务。然而，该作品没有充分验证相机扰动的鲁棒性。在本文中，我们评估现有的模型的鲁棒性使用合成数据集与相机扰动，增加了几个步骤。本文的研究为在实际应用中引入鱼眼相机的三维姿态估计提供了有益的知识。
: 3D egocentric human pose estimations from a mounted fisheye camera have been developed following the advances in convolutional neural networks and synthetic data generations. The camera captures different images that are affected by the optical properties, the mounted position, and the camera perturbations caused by body motion. Therefore, data collecting and model training are main challenges to estimate 3D ego-pose from a mounted fisheye camera. Past works proposed synthetic data generations and two-step estimation model that consisted of 2D human pose estimation and subsequent 3D lift-up to overcome the tasks. However, the works insufficiently verify robustness for the camera perturbations. In this paper, we evaluate existing models for robustness using a synthetic dataset with the camera perturbations that increases in several steps. Our study provides useful knowledges to introduce 3D ego-pose estimation for a mounted fisheye camera in practical.