深度可分离卷积和人质心引导联合分组的有效人体姿态估计

在本文中，我们提出了一种有效的二维人体姿势估计方法。提出了一种新的基于深度可分离卷积的ResBlock，并代替了Hourglass网络中的原始卷积。..

Efficient Human Pose Estimation with Depthwise Separable Convolution and Person Centroid Guided Joint Grouping

In this paper, we propose efficient and effective methods for 2D human pose estimation. A new ResBlock is proposed based on depthwise separable convolution and is utilized instead of the original one in Hourglass network.It can be further enhanced by replacing the vanilla depthwise convolution with a mixed depthwise convolution. Based on it, we propose a bottom-up multi-person pose estimation method. A rooted tree is used to represent human pose by introducing person centroid as the root which connects to all body joints directly or hierarchically. Two branches of sub-networks are used to predict the centroids, body joints and their offsets to their parent nodes. Joints are grouped by tracing along their offsets to the closest centroids. Experimental results on the MPII human dataset and the LSP dataset show that both our single-person and multi-person pose estimation methods can achieve competitive accuracies with low computational costs.