IEEE Trans Image Process. 2017 Feb;26(2):927-941. doi: 10.1109/TIP.2016.2639441. Epub 2016 Dec 14.
Articulated human pose estimation from monocular image is a challenging problem in computer vision. Occlusion is a main challenge for human pose estimation, which is largely ignored in popular tree structured models. The tree structured model is simple and convenient for exact inference, but short in modeling the occlusion coherence especially in the case of self-occlusion. We propose an occlusion relational graphical model, which is able to model both self-occlusion and occlusion by the other objects simultaneously. The proposed model can encode the interactions between human body parts and objects, and enables it to learn occlusion coherence from data discriminatively. We evaluate our model on several public benchmarks for human pose estimation, including challenging subsets featuring significant occlusion. The experimental results show that our method is superior to the previous state-of-the-arts, and is robust to occlusion for 2D human pose estimation.
从单目图像进行人体关节姿态估计是计算机视觉中的一个具有挑战性的问题。遮挡是人体姿态估计的主要挑战，在流行的树形结构模型中很大程度上被忽视了。树形结构模型简单方便进行精确推理，但在建模遮挡连贯性方面存在不足，尤其是在自遮挡的情况下。我们提出了一种遮挡关系图形模型，它能够同时对自遮挡和被其他物体遮挡进行建模。所提出的模型可以编码人体各部位与物体之间的相互作用，并使其能够从数据中判别性地学习遮挡连贯性。我们在几个用于人体姿态估计的公共基准上评估我们的模型，包括具有显著遮挡的具有挑战性的子集。实验结果表明，我们的方法优于先前的最先进方法，并且对于二维人体姿态估计中的遮挡具有鲁棒性。
