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的身体模型拟合到图像中的人的问题。 由于身体姿势，衣服，照明，观点和背景混乱引起的外观差异很大，因此这项任务具有挑战性。 与当前试图适合每个图像的当前方法不同，此方法在部分身体模型的一个空间内使用机会搜索，以发现当前可见并以高信心检测到的那些身体部位。 不试图安装封闭或可见的部分不适合犯错的机会，因此随后的过程可以依靠接收高质量的部分模型解决方案。 使用高级子例程的随机搜索技术提出了候选人体配置，以搜索可见的身体部位的数量和配置来搜索全球最佳解决方案，从而消除了对接近初始估计的需求，并可以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单个身体部位的图像分割提供了自上而下的指导，比简单的参数化模型或自下而上的分割可以更好地描绘身体形状。 该方法的实现正在与使用公开可用数据集的现有工作进行比较。 从静止图像中对躯干和四肢的稳健分割提供了人体的自然表示，可以对诸如人类活动识别和交互式智能空间内无标记的身体跟踪等任务产生广泛的影响。