论文标题
有效的人姿势估计，可分开卷积和人体质心引导分组
Efficient Human Pose Estimation with Depthwise Separable Convolution and Person Centroid Guided Joint Grouping
论文作者
论文摘要
在本文中，我们提出了2D人姿势估计的有效方法。提出了基于深度可分离卷积的新重新建筑，并在沙漏网络中使用了原始的卷积。可以通过用混合深度卷积代替香草深度卷积来进一步增强。基于它，我们提出了一种自下而上的多人姿势估计方法。生根树用于通过引入人质心作为直接或分层连接到所有身体关节的根来表示人姿势。子网络的两个分支用于预测质心，身体关节及其偏移到其父节点。关节是通过沿其偏移到最接近的质心进行追踪来分组的。 MPII人类数据集和LSP数据集的实验结果表明，我们的单人和多人姿势估计方法都可以实现竞争精度，而计算成本较低。
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.
