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相关概念视频

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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相关实验视频

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使用深度和红外图像对车辆乘客的三维姿势估计.

Anuj Tambwekar1, Byoung-Keon D Park2, Arpan Kusari2

  • 1Department of Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109, USA.

Sensors (Basel, Switzerland)
|September 14, 2024
PubMed
概括

这项研究引入了使用深度和红外图像在车辆中的3D人体姿势估计的新方法. 这种方法在最小的手动注释下实现了高精度,提高了自动驾驶中乘客的安全性.

关键词:
李达尔 (LiDAR) 是一种激光雷达.计算机视觉 计算机视觉深度传感器 - 深度传感器姿势估计 姿势估计车辆的乘客安全.

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科学领域:

  • 计算机视觉 计算机视觉
  • 机器人技术 机器人技术 机器人技术
  • 人与计算机的交互

背景情况:

  • 位置估计对于自动驾驶汽车的安全性和用户体验至关重要.
  • 传统的彩色图像方法与3D数据,遮蔽和照明作斗争.
  • 深度图像提供3D信息和照明不变性,但缺乏足够的研究来估计姿势.

研究的目的:

  • 开发一种新的3D人体姿势估计方法,仅使用深度和红外 (IR) 图像.
  • 为了应对有限的注释深度数据对培训的挑战,提出估计模型.
  • 通过精确的乘客姿势检测,提高半自动和完全自动驾驶汽车的乘客安全.

主要方法:

  • 一个三阶段的微调过程,使用模拟,近似和有限的手动注释数据.
  • 利用深度和红外图像数据进行可靠的3D姿势估计.
  • 训练一个用于车载乘客姿势检测的模型.

主要成果:

  • 在所有关节实现了准确的3D姿势估计,平均误差低于10厘米.
  • 需要少于100个手动注释的样本才能有效地训练模型.
  • 展示了第一项专注于仅使用深度和红外线数据检测车辆乘客姿势的研究.

结论:

  • 拟议的方法是可行的和有效的3D人体姿势估计使用深度和红外图像.
  • 这种方法大大减少了需要大量手动注释的需求.
  • 为自动驾驶汽车的乘客安全系统改进铺平了道路.