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首页> 外文会议>European Conference on Computer Vision(ECCV 2004) pt.4; 20040511-20040514; Prague; CZ >Human Pose Estimation Using Learnt Probabilistic Region Similarities and Partial Configurations
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Human Pose Estimation Using Learnt Probabilistic Region Similarities and Partial Configurations

机译：使用学习的概率区域相似性和部分配置进行人体姿态估计

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摘要

A model of human appearance is presented for efficient pose estimation from real-world images. In common with related approaches, a high-level model defines a space of configurations which can be associated with image measurements and thus scored. A search is performed to identify good configuration(s). Such an approach is challenging because the configuration space is high dimensional, the search is global, and the appearance of humans in images is complex due to background clutter, shape uncertainty and texture. The system presented here is novel in several respects. The formulation allows differing numbers of parts to be parameterised and allows poses of differing dimensionality to be compared in a principled manner based upon learnt likelihood ratios. In contrast with current approaches, this allows a part based search in the presence of self occlusion. Furthermore, it provides a principled automatic approach to other object occlusion. View based probabilistic models of body part shapes are learnt that represent intra and inter person variability (in contrast to rigid geometric primitives). The probabilistic region for each part is transformed into the image using the configuration hypothesis and used to collect two appearance distributions for the part's foreground and adjacent background. Likelihood ratios for single parts are learnt from the dissimilarity of the foreground and adjacent background appearance distributions. It is important to note the distinction between this technique and restrictive foreground/background specific modelling. It is demonstrated that this likelihood allows better discrimination of body parts in real world images than contour to edge matching techniques. Furthermore, the likelihood is less sparse and noisy, making coarse sampling and local search more effective. A likelihood ratio for body part pairs with similar appearances is also learnt. Together with a model of inter-part distances this better describes correct higher dimensional configurations. Results from applying an optimization scheme to the likelihood model for challenging real world images are presented.
机译：提出了一种人类外观模型，用于根据真实世界的图像进行有效的姿势估计。与相关方法一样，高级模型定义了一个配置空间，该配置空间可以与图像测量相关联并因此得到评分。执行搜索以识别良好的配置。这种方法具有挑战性，因为配置空间是高维的，搜索是全局的，并且由于背景混乱，形状不确定性和纹理，图像中人的外观很复杂。这里介绍的系统在几个方面都是新颖的。该公式允许对不同数量的零件进行参数化，并允许基于学到的似然比，以有原则的方式比较不同尺寸的姿势。与当前的方法相反，这允许在存在自闭塞的情况下进行基于零件的搜索。此外，它为其他对象遮挡提供了一种原则上自动的方法。学习了基于视图的身体部位形状的概率模型，这些模型表示人际和人际之间的变异性（与刚性几何图元相反）。使用配置假设将每个零件的概率区域转换为图像，并用于收集零件前景和相邻背景的两个外观分布。从前景和相邻背景外观分布的不相似中获悉单个零件的似然比。重要的是要注意此技术与限制性前景/背景特定建模之间的区别。证明了这种可能性比轮廓到边缘匹配技术能够更好地区分现实世界图像中的身体部位。此外，这种可能性更少稀疏和嘈杂，从而使粗采样和局部搜索更加有效。还学习了具有相似外观的身体部位对的似然比。结合零件间距离模型，可以更好地描述正确的高尺寸配置。给出了将优化方案应用于可能性模型以挑战现实世界图像的结果。

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