Nonlinear Spatiotemporal Models for Decomposing Style Variations using Kernel Methods

机器人和计算机视觉项目摘要提案编号：0328991标题：使用核方法分解风格变化的非线性时空模型PI：埃尔加马尔，这项研究的最终目标是模拟人类外表的变化（形状和强度），这是由于通过生成时空模型执行的动作，该生成时空模型明确地分解了由于个性化风格的影响而引起的变化（空间和时间）的人执行的动作。学习这样的模型将有助于一个统一的框架，同时解决某些人体运动分析问题，包括：1）提供跟踪的时空先验。由于个人风格的影响是明确分解的，这些先验可以专门用于特定的跟踪主题。 2)以将对标识执行动作的主体有用的方式参数化个性化风格效果。3)检测时空动作异常值。 与这一最终目标相一致，该提案解决了风格变化对形状方面的外观变化的影响，即，人类的剪影人体轮廓（形状）变形被认为是一种全球形式的外观变化，携带足够的信息，可以用于进一步分析人体运动。在每个时刻所观察到的人体轮廓被认为是从通用时空模型导出的形状，该通用时空模型可以通过正交风格变化模式的显式分解来专门用于跟踪特定的人。由于轮廓随着时间的推移经历拓扑变化，地标（特征）之间的对应关系并不总是可行的。因此，研究将集中在不需要建立特征对应的全局形状表示。 研究将集中在使用显式和基于内核的隐式非线性映射方法，映射的轮廓可以使用多线性张量分解分解成正交因子，为每个影响形状变形的因素，如身体姿势，空间风格和时间风格的基础。

Robotics and Computer Vision ProgramABSTRACTProposal #: 0328991Title: Nonlinear Spatiotemporal Models for Decomposing Style Variations using Kernel MethodsPI: Elgammal, AhmedRutgers Univ New BrunswickThe ultimate goal of this research is to model the changes in human appearance (shape and intensity) due to action being performed through generative spatiotemporal models that explicitly decomposes the variations due to the effects of personalized style (spatial and temporal) of the human performing the action. Learning such models would facilitate a unified framework for simultaneously solving of certain human motion analysis problem including: 1) providing spatiotemporal priors for tracking. Since the effect of personal style is explicitly decomposed, these priors can be specialized to the particular tracked subject. 2) parameterizing the personalized style effect in a way that will be useful for identifying the subject performing the action. 3) Detecting spatiotemporal action outliers. Inline with this ultimate goal, this proposal addresses the effect of style variations on appearance changes in terms of shape, i.e., human silhouettes. Human silhouette (shape) deformation is considered as a global form of appearance changes that carries sufficient information that can be exploited for further analysis of human motion. The observed human silhouette at each time instant is considered as a shape derived from a generic spatiotemporal model that can be specialized to the particular human being tracked through explicit decomposition of orthogonal style variation modes. Since the silhouettes undergo topological changes over time, correspondences between landmarks (features) are not always feasible. Therefore, the research will focus on global shape representations that do not require establishing feature correspondences. The research will focus on the use of explicit and kernel-based implicit nonlinear mapping approaches where the mapped silhouettes can be decomposed using multi-linear tensor decomposition into orthogonal factors given bases for each factor affecting the shape deformation such as body pose, spatial style, and temporal style.