3D Pictorial Structures Revisited: Multiple Human Pose Estimation
3D Pictorial Structures Revisited: Multiple Human Pose Estimation
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DOI：
10.1109/tpami.2015.2509986
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发表时间：
2016-10-01
影响因子：
23.6
通讯作者：
Ilic, Slobodan
中科院分区：
文献类型：
--
作者：
Belagiannis, Vasileios;Amin, Sikandar;Ilic, Slobodan
我们解决的问题，从多个视图的多个人的3D姿态估计。从单个到多个人类姿态估计以及从2D到3D空间的转变是具有挑战性的，这是由于在不预先知道人类的身份时大得多的状态空间、遮挡和跨视图模糊性。为了解决这些问题，我们首先创建一个减少的状态空间，通过三角测量的部分检测器为每个摄像机视图获得的相应对的身体部位。为了解决三角测量后多个人的错误和混合部分的歧义，以及来自假阳性检测的歧义，我们引入了一个3D图像结构（3DPS）模型。我们的模型建立在多视图一元潜力，而先验模型集成到成对和三元势函数。为了平衡电位的影响，使用结构化SVM（SSVM）学习模型参数。该模型是通用的，适用于单个和多个人体姿态估计。为了评估我们的模型对单个和多个人体姿势的估计，我们依赖于四个不同的数据集。我们首先分析的潜力的贡献，然后比较我们的结果与相关的工作，我们表现出上级的性能。
We address the problem of 3D pose estimation of multiple humans from multiple views. The transition from single to multiple human pose estimation and from the 2D to 3D space is challenging due to a much larger state space, occlusions and across-view ambiguities when not knowing the identity of the humans in advance. To address these problems, we first create a reduced state space by triangulation of corresponding pairs of body parts obtained by part detectors for each camera view. In order to resolve ambiguities of wrong and mixed parts of multiple humans after triangulation and also those coming from false positive detections, we introduce a 3D pictorial structures (3DPS) model. Our model builds on multi-view unary potentials, while a prior model is integrated into pairwise and ternary potential functions. To balance the potentials' influence, the model parameters are learnt using a Structured SVM (SSVM). The model is generic and applicable to both single and multiple human pose estimation. To evaluate our model on single and multiple human pose estimation, we rely on four different datasets. We first analyse the contribution of the potentials and then compare our results with related work where we demonstrate superior performance.