Research on single-person pose estimation based on deep neural networks has recently witnessed progress in both accuracy and execution efficiency. However, multiperson pose estimation is still a challenging topic, partially because the object regions are selected greedily from proposals via class-agnostic nonmaximum suppression (NMS), and the misalignment in the redundant detection yields inaccurate human poses. Therefore, we consider how to obtain the optimal input in human pose estimation under conditions in which intermediate label information is not available. As supervised learning–based alignment does not generalize well to unseen samples in the human pose space, in this article, we present a mask-aware deep reinforcement learning approach to modify the detection result. We use mask information to remove the adverse effects from the cluttered background and to select the optimal action according to the revised reward function. We also propose a new regularization term to punish joints that are outside of the silhouette region in the human pose estimation stage. We evaluate our approach on the MPII Multiperson dataset and the MS-COCO Keypoints Challenge. The results show that our approach yields competing inference results when it is compared to the other state-of-the-art approaches.

中文翻译：

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通过面具感知深度强化学习改进多人姿势估计

基于深度神经网络的单人姿态估计研究最近在准确性和执行效率方面取得了进展。然而，多人姿态估计仍然是一个具有挑战性的话题，部分原因是通过与类别无关的非极大值抑制 (NMS) 从提案中贪婪地选择对象区域，并且冗余检测中的错位会产生不准确的人体姿态。因此，我们考虑如何在中间标签信息不可用的情况下获得人体姿态估计的最佳输入。由于基于监督学习的对齐不能很好地推广到人体姿态空间中的未见样本，因此在本文中，我们提出了一种掩模感知深度强化学习方法来修改检测结果。我们使用掩码信息从杂乱的背景中去除不利影响，并根据修改后的奖励函数选择最佳动作。我们还提出了一个新的正则化项来惩罚在人体姿态估计阶段超出轮廓区域的关节。我们在 MPII 多人数据集和 MS-COCO 关键点挑战赛上评估我们的方法。结果表明，与其他最先进的方法相比，我们的方法产生了相互竞争的推理结果。