Part-based pose estimation with local and non-local contextual information
Part-based pose estimation with local and non-local contextual information
复制标题
具有局部和非局部上下文信息的基于部位的姿态估计
DOI：
10.1049/iet-cvi.2013.0156
复制
发表时间：
2014
影响因子：
1.7
通讯作者：
谭晓阳
中科院分区：
文献类型：
--
作者：
陈明;谭晓阳
在这项研究中，作者提出了一种新的基于部分的人体姿态估计方法。作者方法的关键思想是通过将本地和非本地上下文信息纳入模型来提高叶部分定位的准确性-这是以前的研究在很大程度上忽略的问题。特别地，它们使用局部上下文信息来减少或消除噪声的影响，而非局部上下文信息有助于提高叶子部分的检测精度。由于更准确的零件定位通常意味着更合理的空间约束，这可能会提高后续优化过程的有效性。此外，它们保持了基于树的模型的基本结构，因此利用了其概念简单性和计算效率高的推理。他们在两个具有挑战性的真实的世界数据集上的实验证明了所提出方法的可行性和有效性。
In this study, the authors propose a new method for part‐based human pose estimation. The key idea of the authors method is to improve the accuracies for leaf parts localisations – an issue that was largely ignored by the previous study – by incorporating both local and non‐local contextual information into the model. In particular, they use the local contextual information to reduce or eliminate the influences of the noises, while the non‐local contextual information helps to improve the detection accuracies of the leaf parts. Since more accurate parts localisations usually mean a more reasonable active set of spatial constraints, this potentially enhances the effectiveness of the subsequent optimisation procedure. Furthermore, they keep the basic structure of the tree‐based model, hence taking advantage of its conceptual simplicity and computationally efficient inference. Their experiments on two challenging real‐world datasets demonstrate the feasibility and the effectiveness of the proposed method.