3D Human Pose Estimation From Multi Person Stereo 360 Scenes
3D Human Pose Estimation From Multi Person Stereo 360 Scenes
复制标题
DOI：
--
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Matthew Shere;Hansung Kim;A. Hilton
中科院分区：
文献类型：
--
作者：
Matthew Shere;Hansung Kim;A. Hilton
本文提出了一种用于一对360摄像机的人体跟踪和3D姿态估计算法。我们使用外观模型和位置数据在室内和室外环境中的复杂多人场景中识别和跟踪个人，并通过优化卷积姿势机（CPM）产生的关节位置上的真实关节长度的骨架来产生时间一致的3D骨架。我们的结果显示，与最先进的深度学习跟踪方法相比，平均提高了22.67%，并且仅使用两个摄像头就能合理估计姿势。
This paper presents a human tracking and 3D pose estimation algorithm for use with a pair of 360 cameras. We identify and track an individual throughout complex, multiperson scenes in both indoor and outdoor environments using appearance models and positional data, and produce a temporally consistent 3D skeleton by optimising a skeleton of realistic joint lengths over joint positions produce by Convolutional Pose Machines (CPMs). Our results show an average improvement of 22.67% over state of the art deep learning approaches for tracking, as well as reasonable estimates for pose using just two cameras.