Nonlinear methods for parametric grouping and modeling of motion

该项目研究从视觉序列中无监督地学习运动模型，恢复详细的运动模型将使运动识别、分析和合成在监视、监控、多媒体以及一般运动理解等领域的许多应用成为可能。 描述运动是困难的，因为它需要运动类别的知识，然而，他们的手动规范是费力的和自动提取加剧了噪声和模糊的测量的人的运动从视频。 这个项目的中心目标是开发方法和算法，用于使用统一的统计框架对从视频序列中获取的运动进行分组和分析。该框架依赖于统计建模，贝叶斯网络（切换线性动态系统）和概率推理的概念，以开发足够详细，质量，可扩展性和鲁棒性的运动模型，可用于（1）将运动分组为感知上合理的类别和风格，（2）识别和预测运动动态，以及（3）可能合成自然的运动。该框架的效用将评估人体或手部运动的序列。在这个项目的过程中开发的新方法和算法可能在涉及序列数据的建模和分组的许多领域具有广泛的适用性，例如生物技术和生命科学。

This project investigates unsupervised learning of motion models from visual sequences.Recovery of detailed motion models would enable numerous applications of motion recognition, analysis and synthesis in areas such as surveillance, monitoring, multimedia, as well as understanding of motion in general. Describing motion is difficult because it requires the knowledge of motion categories; however, their manual specification is laborious and automated extraction is aggravated by noise and ambiguous measurements of the human motion from video. The central goal of this project is to develop methods and algorithms for grouping and analysis of motion acquired from video sequence using a unified statistical framework. The framework relies on concepts from statistical modeling, Bayesian networks (switching linear dynamic systems) and probabilistic reasoning to develop motion models of sufficient detail, quality, scalability and robustness that could be used to (1) group motion into perceptually plausible categories and styles, (2) recognize and predict motion dynamics, and (3) possibly synthesize natural-looking motion. The utility of the framework will be evaluated on sequences of human body or hand motion. The novel methods and algorithms developed in the course of this project may have wide applicability in many areas that involve modeling and grouping of sequence data, such as biotechnology and life sciences.