DHB: A Multiscale Framework for Analyzing Activity Dynamics

DHB：用于分析活动动态的多尺度框架

• 批准号: 0729013
• 负责人: James Hollan
• 金额: $ 74.92万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-15 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0729013&HistoricalAwards=false
• 关键词: DHB; Multiscale; Framework; Analyzing; Activity

This project is exploring the integration of video and multiscale visualization facilities with computer vision techniques to create a flexible open framework to advance analysis of time-based records of human activity. The goals are to (1) accelerate analysis by employing vision-based pattern recognition capabilities to pre-segment and tag data records, (2) increase analysis power by visualizing multimodal activity and macro-micro relationships, and coordinating analysis and annotation across multiple scales, and (3) facilitate shared use of the developing framework with collaborators. Researchers from many disciplines are taking advantage of increasingly inexpensive digital video and storage facilities to assemble extensive data collections of human activity captured in real-world settings. The ability to record and share such data has created a critical moment in the practice and scope of behavioral research. The main obstacles to fully capitalizing on this scientific opportunity are the huge time investment required for analysis using current methods and understanding how to coordinate analyses focused at different scales so as to profit fully from the theoretical perspectives of multiple disciplines. Thus, any research using video or other time-based records in order to document or better understand human activity is a potential beneficiary of this research, and the long range objective is to better understand the dynamics of human activity as a scientific foundation for design.

该项目正在探索将视频和多尺度可视化设施与计算机视觉技术相结合，以创建一个灵活的开放式框架，以推进对人类活动时间记录的分析。其目标是：（1）通过采用基于视觉的模式识别功能来预分割和标记数据记录来加速分析;（2）通过可视化多模态活动和宏微观关系来提高分析能力，并在多个尺度上协调分析和注释;以及（3）促进与合作者共享开发框架。来自许多学科的研究人员正在利用越来越便宜的数字视频和存储设施来收集在现实世界中捕获的人类活动的广泛数据。记录和分享这些数据的能力在行为研究的实践和范围中创造了一个关键时刻。充分利用这一科学机会的主要障碍是使用现有方法进行分析所需的大量时间投资，以及了解如何协调不同尺度的分析，以便从多学科的理论角度充分获益。因此，任何使用视频或其他基于时间的记录来记录或更好地理解人类活动的研究都是这项研究的潜在受益者，长期目标是更好地理解人类活动的动态，作为设计的科学基础。