CDI-Type I: Collaborative Research: A Bio-Inspired Approach to Recognition of Human Movements and Movement Styles

CDI-I 型：协作研究：识别人类运动和运动风格的仿生方法

• 批准号: 0941463
• 负责人: Rene Vidal
• 金额: $ 49.33万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-01-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0941463&HistoricalAwards=false
• 关键词: CDI; Type; Collaborative; Research; Bio

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).The objective of this research is to develop bio-inspired algorithms for recognizing human movements and movement styles in videos of human activities. The approach is based on a new representation of static three-dimensional (3D) shape structure in the ventral visual pathway consisting of configurations of 3D structural fragments. This project uses neural experiments to validate an analogous four-dimensional (4D) representation of moving 3D shapes based on 4D (space and time) structure-in-motion (SiM) fragments. These SiM fragment models are used to develop algorithms for automatically extracting SiM fragments from videos of human activities. Hybrid system identification and clustering techniques are used to learn a dictionary of human movements used for recognition. This dictionary, in turn, influences the design of the neural experiments. The algorithms are evaluated using a real-time tele-immersion system.The intellectual merit of this project is to substantially reduce the gap between human and machine perception of human movements through the interaction between computational and experimental analyses from neuroscience, computer vision, machine learning, and dynamical systems. This project also has the potential to generate advances in machine learning and dynamical systems, by extending classification, clustering and system identification methods to time-series data generated by collections of hybrid dynamical models.Potential broader impacts include applications in surveillance, security, assisted home living, infant care, tele-immersion, and athlete motion analysis. The project also sponsors a competition where small robots are constructed by middle and high-school students from Baltimore, and the EL Alliance program for retaining underrepresented minorities at graduate schools.

该奖项是根据2009年的《美国复苏与再投资法》（公法111-5）资助的。该研究的目的是开发以生物启发的算法来识别人类活动视频中人类运动和运动风格。 该方法基于腹侧视觉途径中静态三维（3D）形状结构的新表示，该途径由3D结构片段组成。 该项目使用神经实验来验证基于4D（空间和时间）内运动（SIM）片段的移动3D形状的类似的四维（4D）表示。 这些SIM片段模型用于开发用于自动从人类活动视频中提取SIM片段的算法。 混合系统识别和聚类技术用于学习用于识别的人类运动的字典。 反过来，该词典影响了神经实验的设计。 使用实时远程放水系统评估算法。该项目的智力优点是通过从神经科学，计算机视觉，机器学习和动态系统的计算和实验分析之间的相互作用来大大减少人类对人类运动的差距。 该项目还具有通过将分类，聚类和系统识别方法扩展到由混合动力学模型集合产生的时间序列数据来实现机器学习和动态系统的进展。在监视，安全性，辅助家庭生活，婴儿护理，电视和临时和运动员运动者运动分析中，更广泛的影响包括在内的时间序列数据。 该项目还赞助了一项竞赛，其中小型机器人是由巴尔的摩的中学和高中生建造的，以及在研究生院保留代表性不足的EL Alliance计划。