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.

本文提出了一种基于图模型和最大后验估计的多人跨视角估计的方法，旨在解决人体关节匹配和模型鲁棒性的问题，并在四个基准数据集上证明了该方法的有效性和优越性。

基于多视角几何的拥挤场景多人三维姿态估计

Multi-person 3D Pose Estimation in Crowded Scenes Based on Multi-View  Geometry

Human pose estimation has recently made significant progress with the
adoption of deep convolutional neural networks. Its many applications have
attracted tremendous interest in recent years. However, many practical
applications require pose estimation for human crowds, which still is a rarely
addressed problem. In this work, we explore methods to optimize pose estimation
for human crowds, focusing on challenges introduced with dense crowds, such as
occlusions, people in close proximity to each other, and partial visibility of
people. In order to address these challenges, we evaluate three aspects of a
pose detection approach: i) a data augmentation method to introduce robustness
to occlusions, ii) the explicit detection of occluded body parts, and iii) the
use of the synthetic generated datasets. The first approach to improve the
accuracy in crowded scenarios is to generate occlusions at training time using
person and object cutouts from the object recognition dataset COCO (Common
Objects in Context). Furthermore, the synthetically generated dataset JTA
(Joint Track Auto) is evaluated for the use in real-world crowd applications.
In order to overcome the transfer gap of JTA originating from a low pose
variety and less dense crowds, an extension dataset is created to ease the use
for real-world applications. Additionally, the occlusion flags provided with
JTA are utilized to train a model, which explicitly distinguishes between
occluded and visible body parts in two distinct branches. The combination of
the proposed additions to the baseline method help to improve the overall
accuracy by 4.7% AP and thereby provide comparable results to current
state-of-the-art approaches on the respective dataset.

本论文针对人群姿态估计的问题，提出了通过数据增强方法、显式识别遮挡的身体部位和使用合成数据集来优化姿态估计。论文的实验结果表明这些方法提高了模型的准确性，使其在人群场景下获得了与当前最先进方法相媲美的结果。