Systems involving human-robot collaboration necessarily require that steps be taken to ensure safety of the participating human. This is usually achievable if accurate, reliable estimates of the human's pose are available. In this paper, we present a deep Predictive Coding (PC) model supporting visual segmentation, which we extend to pursue pose estimation. The model is designed to offer robustness to the type of transient occlusion naturally occurring when human and robot are operating in close proximity to one another. Impact on performance of relevant model parameters is assessed, and comparison to an alternate pose estimation model (NVIDIA's PoseCNN) illustrates efficacy of the proposed approach.

翻译：涉及人机协作的系统必然要求采取措施确保参与人员的安全。这一目标通常需要在获取准确、可靠的人体姿态估计前提下得以实现。本文提出了一种支持视觉分割的深度预测编码（PC）模型，并将其扩展以实现姿态估计。该模型的设计旨在抵御人与机器人在近距离协作时自然出现的瞬时遮挡。我们评估了相关模型参数对性能的影响，并与替代姿态估计模型（NVIDIA的PoseCNN）进行了对比，验证了所提方法的有效性。