Robust human pose estimation from corrupted images with partial occlusions and noise pollutions
Robust human pose estimation from corrupted images with partial occlusions and noise pollutions
复制标题
从具有部分遮挡和噪声污染的损坏图像中进行稳健的人体姿势估计
DOI：
--
复制
发表时间：
2011
期刊：
影响因子：
--
通讯作者：
J. Toyama
中科院分区：
文献类型：
--
作者：
Guoliang Lu;Mineichi Kudo;J. Toyama
在过去的二十年里，从给定的视觉观测鲁棒的人体姿态估计引起了许多关注。然而，这个问题仍然是具有挑战性的，由于suituation，观察经常损坏与部分闭塞或噪声污染或在现实世界中的应用。在本文中，我们提出了估计人体姿态使用鲁棒的轮廓匹配在原始的矩形坐标空间。此外，还利用人体行为模型来确定合理的匹配结果。Weizman数据集上的鲁棒性序列实验结果表明，当姿态观测数据被部分遮挡或噪声污染破坏时，该方法能够鲁棒合理地估计出人体姿态。
Robust human pose estimation from the given visual observations has attracted many attentions in the past two decades. However, this problem is still challenging due to the suituation that observations are often corrupted with partial occlusions or noise pollutions or both in real-world applications. In this paper, we propose to estimate human pose by using robust silhouette matching in original rectangle-coordinate space. In addition, human action model is employed to determinate reasonable matching results. Experimental results on robustness sequence of Weizman dataset reveal that our proposed approach can estimate human pose robustly and reasonably when pose observations are corrupted with partial occlusions or noise pollutions.