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ITR: Providing Intuitive Access to Human Motion Databases

ITR：提供对人体运动数据库的直观访问

基本信息

批准号：
0205224
负责人：
Jessica Hodgins
金额：
$ 51万
依托单位：
Carnegie-Mellon University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2002
资助国家：
美国
起止时间：
2002-09-01 至 2005-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=0205224&HistoricalAwards=false
关键词：
ITR Providing Intuitive Access Human

项目摘要

Our ability to capture human motion data has increased dramaticallymaking possible human motion databases that can support creative andeducational endeavors such as training of athletes, diagnosing andtreating motion disorders, and preserving our cultural heritage. Tohave an impact, however, motion databases must be accessible to a wideaudience, including children and adult users with little programmingexperience. During the time frame of this project, we will undertakean exploration of a performance-based interface for a human motiondatabase. The interface will allow the user to act out the desiredmotion and will require comparing time sequence data from the performance,perhaps with missing or noisy entries, to time sequence data in thedatabase.Retrieval techniques must compute the distance between a sample andarbitrary motion subsequences in the database, making brute forcetechniques prohibitively expensive, particularly for interactiveperformance. Algorithms for dimensionality reduction, featuretransformations, and automatic segmentation will be implemented,distance functions will be compared both quantitatively and via userstudies, and the performance of these algorithms with missing data willbe assessed. The research will be evaluated with traditional databaseretrieval evaluation techniques and through observation and analysis ofusers of the interfaces.

我们捕捉人体运动数据的能力已经大大提高，使人体运动数据库成为可能，这些数据库可以支持创造性和教育性的努力，如运动员的训练，诊断和治疗运动障碍，以及保护我们的文化遗产。然而，要产生影响，运动数据库必须能够被广泛的受众访问，包括儿童和几乎没有编程经验的成人用户。在这个项目的时间范围内，我们将承担一个基于性能的界面为人类motiondatabase的探索。该界面将允许用户表演出所需的动作，并需要将表演中的时间序列数据（可能有缺失或噪声条目）与数据库中的时间序列数据进行比较。检索技术必须计算样本与数据库中任意动作序列之间的距离，这使得暴力破解技术成本过高，特别是对于交互式表演。将实现降维、特征转换和自动分割的算法，将定量地和通过用户研究比较距离函数，并评估这些算法在缺失数据情况下的性能。本研究将采用传统的数据库检索评价技术并通过对界面用户的观察和分析进行评价。