Multi-person pose estimation is an important field in computer vision. Due to the lower time complexity, the bottom-up approaches have recently received more attention in multi-person 2D pose estimation, however, they are more sensitive to challenges in real-world scenarios. In this paper, we propose a multi-person pose estimation algorithm based on the Double Anchor Embedding (DAE), which shows that bottom-up algorithms are still competitive in precision. Firstly, for reducing the modeling difficulty of the detection task we divide the human joints into upper and lower half groups which are internally continuous and highly correlated. Accordingly, a novel joint affinity cue, called Double Anchor Embedding is designed, which can help the network effectively extract the information of both local contexts and global contexts, so that can better cope with occluded scenes and complex postures. Secondly, the parallel greedy joint inference algorithm is proposed to alleviate the mismatching problem of distant joints in the post-processing stage, which can also accelerate the matching process to some extent. Extensive experiments on two challenging datasets demonstrate the effectiveness and potential of our proposed framework, which is comparable to the current state-of-the-art methods.

多人姿势估计是计算机视觉中的重要领域。由于时间复杂度较低，自下而上的方法最近在多人2D姿势估计中受到了更多关注，但是，它们对实际场景中的挑战更加敏感。在本文中，我们提出了一种基于双锚嵌入（DAE）的多人姿势估计算法，该算法表明自下而上的算法在精度上仍具有竞争力。首先，为了减少检测任务的建模难度，我们将人体关节分为内部连续且高度相关的上半部分和下半部分。因此，设计了一种新颖的联合亲和力线索，称为Double Anchor Embedding，它可以帮助网络有效地提取本地上下文和全局上下文的信息，这样可以更好地应对被遮挡的场景和复杂的姿势。其次，提出了并行贪婪联合推理算法，以减轻后处理阶段远距离关节的失配问题，也可以在一定程度上加速匹配过程。在两个具有挑战性的数据集上进行的大量实验证明了我们提出的框架的有效性和潜力，可与当前的最新方法相媲美。