RI: EAGER: Robust Opportunistic Fitting of Partial Body Models

RI：EAGER：部分身体模型的鲁棒机会拟合

• 批准号: 0951386
• 负责人: Robert Collins
• 金额: $ 12.51万
• 依托单位: Pennsylvania State Univ University Park
• 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: 2009-09-15 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0951386&HistoricalAwards=false
• 关键词: RI; EAGER; Robust; Opportunistic; Fitting

This project addresses the problem of fitting an articulated body model to a person in an image. The task is challenging due to large variation in appearance caused by body pose, clothing, illumination, viewpoint and background clutter. Unlike current methods that try to fit a full body model to every image, this approach uses opportunistic search within a space of partial body models to find only those body parts that are currently visible and detected with high confidence. Not trying to fit occluded or poorly visible parts reduces the chances of making a mistake, so subsequent processes can rely on receiving a high-quality partial model solution. A stochastic search technique employing high-level subroutines to propose candidate body configurations searches for the globally optimal solution in terms of number and configuration of visible body parts, removing the need for a close initial estimate and allowing more thorough exploration of the solution space. The proposed partial body configurations also provide top-down guidance for image segmentation of individual body parts, yielding better delineation of body shape than simple parameterized models or bottom-up segmentation. An implementation of the approach is being compared against existing work using publicly available datasets. Robust segmentation of torso and limbs from still images provides a natural representation of the human body that can have broad impact on tasks such as human activity recognition and markerless body tracking within interactive smart spaces.

该项目解决了将铰接式人体模型与图像中的人物拟合的问题。 由于身体姿势、服装、照明、视角和背景杂乱导致外观变化很大，因此这项任务具有挑战性。 与当前尝试将全身模型拟合到每个图像的方法不同，该方法在部分身体模型的空间内使用机会搜索来仅查找当前可见并以高置信度检测到的那些身体部位。 不尝试拟合遮挡或不可见的零件可以减少犯错误的机会，因此后续流程可以依赖于接收高质量的部分模型解决方案。 随机搜索技术采用高级子例程来提出候选身体配置，在可见身体部位的数量和配置方面搜索全局最优解，从而无需进行精确的初始估计，并允许对解决方案空间进行更彻底的探索。所提出的部分身体配置还为各个身体部位的图像分割提供自上而下的指导，比简单的参数化模型或自下而上的分割更好地描绘身体形状。 正在将该方法的实施与使用公开数据集的现有工作进行比较。 从静态图像中对躯干和四肢进行稳健分割可以提供人体的自然表示，这可以对交互式智能空间中的人类活动识别和无标记身体跟踪等任务产生广泛的影响。