RI: EAGER: Robust Opportunistic Fitting of Partial Body Models

该项目解决了将关节式身体模型拟合到图像中的人的问题。 这项任务是具有挑战性的，由于身体姿势，服装，照明，视点和背景杂波引起的外观变化很大。 与试图将全身模型拟合到每个图像的当前方法不同，该方法在部分身体模型的空间内使用机会主义搜索，以仅找到当前可见并且以高置信度检测到的那些身体部位。 不尝试拟合被遮挡或可见性差的零件可以减少出错的可能性，因此后续过程可以依赖于接收高质量的局部模型解决方案。 一种随机搜索技术，采用高层次的子程序，提出候选人的身体配置搜索的全球最佳解决方案的数量和配置的可见的身体部位，消除了需要一个密切的初始估计，并允许更彻底的探索的解决方案空间。所提出的部分身体配置还提供了自上而下的指导，个别身体部位的图像分割，产生更好的描绘身体形状比简单的参数化模型或自下而上的分割。 目前正在利用公开数据集将这一方法的实施情况与现有工作进行比较。 从静态图像中对躯干和四肢进行鲁棒分割提供了人体的自然表示，这可能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。

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.