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

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：找出人体如何运动，跟踪高速公路上的车辆运动或鱼群的运动。 尽管取得了许多进展，这个问题仍然很难，因为突然的，往往是高度非线性的变化和高维的对象的配置空间。 许多先前的工作都集中在建立复杂的物理为基础的模型，在一个“分析综合”的范式占主导地位的专家的领域知识。 当缺乏这种知识时，所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。我们接近的问题，结构化回归，一种新的概括传统的统计方法，专门利用时空结构的数据，以避免需要“分析合成”。 该研究将产生一套支持这种新建模框架的强大技术和计算算法，在依赖于复杂时空域中精确预测模型设计的许多技术和工业领域中，这里开发的工具和技术将具有广泛的适用性，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的研究活动，该项目还提供了对新一代计算机科学家的成功至关重要的高级技术培训。

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.