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

CDI-I 型：协作研究：一种识别人类动作及其风格的仿生方法

• 批准号: 0941382
• 负责人: Ruzena Bajcsy
• 金额: $ 24.67万
• 依托单位: University of California-Berkeley
• 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=0941382&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年美国复苏和再投资法案（公法111-5）资助的。本研究的目的是开发生物启发算法来识别人类活动视频中的人类运动和运动风格。该方法基于由三维结构碎片组成的腹侧视觉通路中静态三维（3D）形状结构的新表示。该项目使用神经实验来验证基于4D（空间和时间）运动结构（SiM）片段的移动3D形状的类似四维（4D）表示。这些SiM片段模型用于开发从人类活动视频中自动提取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动作字典。这个词典反过来又影响了神经实验的设计。使用实时远程沉浸系统对算法进行了评估。这个项目的智力价值在于，通过神经科学、计算机视觉、机器学习和动力系统的计算和实验分析之间的相互作用，大大减少了人类和机器对人类运动感知之间的差距。通过将分类、聚类和系统识别方法扩展到由混合动态模型集合生成的时间序列数据，该项目也有可能在机器学习和动态系统方面取得进展。潜在的更广泛的影响包括在监视、安全、辅助家庭生活、婴儿护理、远程沉浸和运动员运动分析方面的应用。该项目还赞助了一项竞赛，由巴尔的摩的中学生和高中生制作小型机器人，以及EL联盟项目，旨在保留研究生院中代表性不足的少数民族。