RI: Small: Modeling and Parsing Time Series for Causal Analysis with Application to Action Interpretation in Video of Natural and Man-made Environments

RI：小型：因果分析的建模和解析时间序列及其应用于自然和人造环境视频中的动作解释

• 批准号: 1018922
• 负责人: Stefano Soatto
• 金额: $ 14万
• 依托单位: University of California-Los Angeles
• 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: 2010-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1018922&HistoricalAwards=false
• 关键词: RI; Small; Modeling; Parsing; Time

This project tackles the development of new tools for the semantic analysis of temporal signals, in particular (but not restricted to) video sequences. While most of the emphasis in video analysis so far has been at the low-level, the investigators plan to explore the use of Causal Analysis to perform inference and decisions to analyze video signals. The challenge in this project is to bridge the gap between basic descriptor at the signal level and Causal Calculus, that acts on semantically meaningful representations. In particular, long-range prediction, not just short-range continuous extrapolation, requires the development of new tools that allow "interventions" into the model. How would the state "X" evolve if event "Y" were to occur? To attain the goals set forth in the proposal, the investigators must tackle fundamental problems in the analysis of time series, both at the low/mid-level (defining a proper notion of ``distance'' between time series that respects their intrinsic dynamics), at the mid-level (defining clustering schemes for action segments), and at the high-level (develop action semantics in an abductive framework). During this pilot one-year project, the investigators plan to explore the feasibility of using causal analysis for performing long-range temporal prediction of events and actions from visual data. Sample applications that are impacted in case of success are broad ranging from surveillance to environmental monitoring to driver assistance in transportation, with significant societal impact in reducing traffic accidents.

该项目致力于开发用于时间信号语义分析的新工具，特别是（但不限于）视频序列。虽然到目前为止，视频分析的大部分重点都在低水平上，但研究人员计划探索使用因果分析来进行推理和决策，以分析视频信号。这个项目的挑战是弥合信号级的基本描述符和因果演算之间的差距，因果演算作用于语义上有意义的表示。特别是，长期预测，而不仅仅是短期连续外推，需要开发允许对模型进行“干预”的新工具。如果事件“Y”发生，状态“X”将如何演变？为了实现在建议中提出的目标，调查人员必须解决的基本问题，在分析的时间序列，无论是在低/中级（定义一个适当的概念之间的“距离”的时间序列，尊重其内在的动态），在中级（定义聚类方案的行动部分），并在高层次（发展行动语义的溯因框架）。在这个为期一年的试点项目中，研究人员计划探索使用因果分析对视觉数据中的事件和行为进行长期时间预测的可行性。在成功的情况下受到影响的示例应用范围很广，从监视到环境监测到交通中的驾驶员辅助，在减少交通事故方面具有重大的社会影响。