In this paper, we address the problem of 2D-3D pose estimation. Specifically, we propose an approach to jointly track a rigid object in a 2D image sequence and to estimate its pose (position and orientation) in 3D space. We revisit a joint 2D segmentation/3D pose estimation technique, and then extend the framework by incorporating a particle filter to robustly track the object in a challenging environment, and by developing an occlusion detection and handling scheme to continuously track the object in the presence of occlusions. In particular, we focus on partial occlusions that prevent the tracker from extracting an exact region properties of the object, which plays a pivotal role for region-based tracking methods in maintaining the track. To this end, a dynamical choice of how to invoke the objective functional is performed online based on the degree of dependencies between predictions and measurements of the system in accordance with the degree of occlusion and the variation of the object's pose. This scheme provides the robustness to deal with occlusions of an obstacle with different statistical properties from that of the object of interest. Experimental results demonstrate the practical applicability and robustness of the proposed method in several challenging scenarios.

中文翻译：

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用于刚性 2D-3D 姿态跟踪的粒子过滤器和遮挡处理。

在本文中，我们解决了 2D-3D 姿态估计的问题。具体来说，我们提出了一种在 2D 图像序列中联合跟踪刚性对象并在 3D 空间中估计其姿态（位置和方向）的方法。我们重新审视了联合 2D 分割/3D 姿态估计技术，然后通过结合粒子滤波器来扩展框架以在具有挑战性的环境中稳健地跟踪对象，并通过开发遮挡检测和处理方案以在存在的情况下持续跟踪对象闭塞。特别是，我们专注于阻止跟踪器提取对象的精确区域属性的部分遮挡，这对于基于区域的跟踪方法在保持轨迹方面起着关键作用。为此，根据遮挡程度和对象姿态的变化，基于系统预测和测量之间的依赖程度，在线执行如何调用目标函数的动态选择。该方案提供了处理具有与感兴趣对象不同统计特性的障碍物遮挡的鲁棒性。实验结果证明了所提出的方法在几个具有挑战性的场景中的实际适用性和鲁棒性。