RI: Small: Novel structured regression approaches to high-dimensional motion analysis

RI：小：用于高维运动分析的新颖结构化回归方法

• 批准号: 0916812
• 负责人: Vladimir Pavlovic
• 金额: $ 37.09万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0916812&HistoricalAwards=false
• 关键词: RI; Small; Novel; structured; regression

The ability to estimate motion of objects from video is a fundamental scientific problem that arises in many tasks: finding out how the human body moves, tracking vehicles movements on a highway or the motility of schools of fish. Despite many advancements the problem remains hard because of sudden, often highly nonlinear changes and the high dimensionality of the object's configuration spaces. Much prior work has focused on building complex physics-based models, in an "analysis-by-synthesis" paradigm dominated by expert's domain knowledge. When such knowledge is lacking, the resulting models may produce inaccurate predictions. To address these issues, this project investigates a new paradigm of using limited amounts of carefully collected data to learn direct predictive models of high-dimensional motion. We approach the problem as that of the structured regression, a novel generalization of traditional statistical methods that specifically exploits the spatio-temporal structure of the data to avoid the need for "analysis-by-synthesis". This research will result in a set of robust techniques and computational algorithms that support this new modeling framework.The tools and techniques developed here will have wide applicability in many areas of technology and industry that rely on design of accurate prediction models in complex space-time domains, leading to more general and sustainable forecasting solutions. Through engagement of graduate and undergraduate students in key research activities, the project also provides advanced technical training vital for success of a new generation of computer scientists.

从视频中估计物体运动的能力是一个基本的科学问题，它出现在许多任务中：找出人体如何移动，跟踪高速公路上的车辆移动，或者鱼群的运动。尽管取得了许多进展，但由于突然的、通常是高度非线性的变化以及对象的高维构形空间，这个问题仍然很难解决。以前的许多工作都集中在建立复杂的基于物理的模型上，采用以专家的领域知识为主导的“综合分析”范式。当缺乏这样的知识时，由此产生的模型可能会产生不准确的预测。为了解决这些问题，该项目研究了一种新的范式，即使用有限数量的仔细收集的数据来学习高维运动的直接预测模型。我们把这个问题归结为结构回归问题，这是对传统统计方法的一种新的概括，它特别利用了数据的时空结构，从而避免了“按综合分析”的需要。这项研究将产生一套稳健的技术和计算算法来支持这一新的建模框架。这里开发的工具和技术将在许多依赖于复杂时空域中准确预测模型设计的技术和工业领域具有广泛的适用性，从而产生更通用和可持续的预测解决方案。通过让研究生和本科生参与关键研究活动，该项目还提供对新一代计算机科学家的成功至关重要的高级技术培训。