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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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通过仔细的跨维度匹配来估计单视图多人姿势.

Wei Tian1, Zhong Gao1, Dayi Tan1

  • 1Institute of Intelligent Vehicles, School of Automotive Studies, Tongji University, Shanghai, China.

Frontiers in neuroscience
|August 4, 2023
PubMed
概括

本研究引入了一种用于多人姿势估计的新型k-block架构,通过使用整个热图和SMPL模型来提高准确性. 该方法增强了复杂环境中的实时人类姿势估计.

科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 人与计算机的交互

背景情况:

  • 基于视觉的人类姿势估计对于AR,动作识别和HCI至关重要.
  • 关键点检测方法通常通过关注小热图区域来浪费信息,从而限制了优化.
  • 现有的方法在信息利用和模型优化方面扎.

研究的目的:

  • 开发一个先进的多人姿势估计架构.
  • 为了提高关键点估计的准确性和效率.
  • 为了在复杂的场景中实现实时姿势估计.

主要方法:

  • 设计了一个新的k-block架构,在整个热图上使用投票机制.
  • 集成了SMPL 3D人体模型,用于代姿势校正.
  • 利用整个热图中的信息,同时进行关键点和不确定性推断.

主要成果:

  • 在3DPW数据集上实现了最先进的性能.
  • 平均每关节位置误差 (MPJPE) 改进了大约8毫米.
  • 每个关节位置平均误差 (PA-MPJPE) 改进了大约5毫米.

结论:

关键词:
专注学习 专注学习交叉维度匹配的交叉维度匹配预测关键点的预测多人姿势估计多人姿势估计单个图像的姿势估计估计.

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  • 拟议的架构有效地解决了信息浪费的问题.
  • 集成SMPL模型通过利用人体结构来提高姿势准确性.
  • 实时功能为复杂的现实应用中多人姿势估计开辟了新的途径.