论文标题
基于多视图几何形状的拥挤场景中的多人3D姿势估计
Multi-person 3D Pose Estimation in Crowded Scenes Based on Multi-View Geometry
论文作者
论文摘要
在当前多人多人相机3D人体姿势估计方法中，表极约束是特征匹配和深度估计的核心。尽管这种表述在稀疏的人群场景中的表现令人满意，但在密集的人群情况下，其有效性经常受到挑战，主要是由于两个歧义来源。首先是由关节和异性线之间的欧几里得距离所提供的简单提示产生的人类关节的不匹配。第二个是由于最小二乘最小化，因此缺乏鲁棒性。在本文中，我们偏离了多人3D姿势估计表格，而是将其重新制定为人群姿势估计。我们的方法由两个关键组成部分组成：快速跨视图匹配的图形模型，以及最大的后验（MAP）估计器，用于重建3D人类姿势。我们证明了我们提出的方法在四个基准数据集上的有效性和优势。
Epipolar constraints are at the core of feature matching and depth estimation in current multi-person multi-camera 3D human pose estimation methods. Despite the satisfactory performance of this formulation in sparser crowd scenes, its effectiveness is frequently challenged under denser crowd circumstances mainly due to two sources of ambiguity. The first is the mismatch of human joints resulting from the simple cues provided by the Euclidean distances between joints and epipolar lines. The second is the lack of robustness from the naive formulation of the problem as a least squares minimization. In this paper, we depart from the multi-person 3D pose estimation formulation, and instead reformulate it as crowd pose estimation. Our method consists of two key components: a graph model for fast cross-view matching, and a maximum a posteriori (MAP) estimator for the reconstruction of the 3D human poses. We demonstrate the effectiveness and superiority of our proposed method on four benchmark datasets.
