课题基金 / 基金详情

High Accuracy Non-Optical Motion Capture

High Accuracy Non-Optical Motion Capture
高精度非光学动作捕捉
批准号:
RTI-2021-00282
负责人:
Pai, Dinesh
金额:
$6.55万
依托单位国家:
加拿大
项目类别:
Research Tools and Instruments
财政年份:
2020
资助国家:
加拿大
项目状态:
已结题
起止时间:
2020-01-01 至 2021-12-31

项目摘要

项目成果

Pai, Dinesh的其他基金

相似基金

相关文献

中文摘要
翻译
点击翻译按钮获取中文摘要
英文摘要
Measuring the motion of objects in 3D dimensions, including the motion of the human body, is an essential task in many applications. Almost all current methods, including the NSERC funded research of the applicants, use optical methods. These rely on cameras for measuring motion, since high quality cameras are ubiquitous, easy to use, and relatively inexpensive. However, they suffer from problems with occlusion of line of sight and have lower accuracy under the typical conditions of measuring human motion (errors >> 1mm). To validate the accuracy of camera-based research systems, and to train them using machine learning techniques, we need ground truth data that are more accurate than our systems and do not suffer from the same limitations. We will acquire two complementary instruments that have high accuracy (accuracy << 1mm) and do not suffer from visual occlusion. One instrument tracks 3D motion using magnetic fields, and another using a mechanical armature connected to the tracked object. No measurement system is perfect - by acquiring two complementary systems we can demonstrate the accuracy and robustness of our research systems under a wide range of conditions. The instruments will be used by the applicants and other researchers in the fields of computer graphics, computer vision, human-computer interfaces, and biomechanics, to develop better motion capture systems, including those using cameras and other novel sensing modalities. Specifically, they will be used to rigorously test our research systems and to quantitatively assess the accuracy of these systems. The instruments will also be used to create a database of ground truth motion that could be used for calibrating camera-based motion capture systems and to generate data for training deep neural networks to track motion from video. Such systems benefit Canada by making low cost, high quality motion capture systems for a range of applications including patient monitoring systems, natural computer interfaces, virtual reality and augmented reality headsets, computer games, and visual effects.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
Computational Models of Humans
  • 批准号:
    RGPIN-2017-04604
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $12.38万
  • 财政年份:
    2021
  • 负责人:
    Pai, Dinesh
  • 依托单位:
Computational Models of Humans
  • 批准号:
    RGPIN-2017-04604
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $6.19万
  • 财政年份:
    2020
  • 负责人:
    Pai, Dinesh
  • 依托单位:
Sensorimotor Computation
  • 批准号:
    1000228443-2012
  • 项目类别:
    Canada Research Chairs
  • 资助金额:
    $3.64万
  • 财政年份:
    2020
  • 负责人:
    Pai, Dinesh
  • 依托单位:
Computational Models of Humans
  • 批准号:
    RGPIN-2017-04604
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $6.19万
  • 财政年份:
    2019
  • 负责人:
    Pai, Dinesh
  • 依托单位:
国内基金
海外基金
Non-CG DNA甲基化平衡大豆产量和SMV抗性的分子机制
  • 批准号:
    32301796
  • 项目类别:
    青年科学基金项目
  • 资助金额:
    30万元
  • 批准年份:
    2023
  • 负责人:
    寻红卫
  • 依托单位:
long non-coding RNA(lncRNA)-activatedby TGF-β(lncRNA-ATB)通过成纤维细胞影响糖尿病创面愈合的机制研究
  • 批准号:
    LQ23H150003
  • 项目类别:
    省市级项目
  • 资助金额:
    --
  • 批准年份:
    2023
  • 负责人:
    厉怡
  • 依托单位:
染色体不稳定性调控肺癌non-shedding状态及其生物学意义探索研究
  • 批准号:
    82303936
  • 项目类别:
    青年科学基金项目
  • 资助金额:
    30万元
  • 批准年份:
    2023
  • 负责人:
    张嘉涛
  • 依托单位:
变分法在双临界Hénon方程和障碍系统中的应用
  • 批准号:
    12301258
  • 项目类别:
    青年科学基金项目
  • 资助金额:
    30.00万元
  • 批准年份:
    2023
  • 负责人:
    王聪
  • 依托单位: