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.

这个项目解决的问题是将一个关节身体模型与图像中的一个人相匹配。由于身体姿势、服装、照明、视点和背景杂乱导致外观变化很大，这项任务具有挑战性。与当前试图将全身模型匹配到每一张图像的方法不同，该方法使用机会主义搜索在部分身体模型空间中仅找到当前可见并以高置信度检测到的身体部分。不尝试拟合被遮挡或不可见的部分会减少出错的可能性，因此后续过程可以依赖于收到高质量的局部模型解决方案。随机搜索技术使用高级子例程来建议候选身体配置，根据可见身体部位的数量和配置来搜索全局最优解，从而消除了对接近的初始估计的需要，并允许更彻底地探索解空间。所提出的身体局部配置还为身体各个部位的图像分割提供了自上而下的指导，与简单的参数化模型或自下而上的分割相比，产生了更好的身体形状描绘。该方法的实施正在与使用公开可用的数据集的现有工作进行比较。从静止图像中稳健地分割躯干和四肢提供了人体的自然表示，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影响。