论文标题
简单的多分辨率表示人类姿势估计
Simple Multi-Resolution Representation Learning for Human Pose Estimation
论文作者
论文摘要
人类姿势估计 - 识别给定图像中人类关键的过程 - 是计算机视觉中最重要的任务之一，并且具有广泛的应用，包括运动诊断，监视或自动驾驶工具。由于深度学习的发展，人类关键点预测的准确性越来越有所提高。大多数现有方法通过生成热图来求解人类姿势估计，其中ITH热图指示ITH关键点的位置置信度。在本文中，我们介绍了针对人类关键点预测的多分辨率表示学习的新型网络结构。在学习过程中的不同分辨率下，我们的网络分支并使用额外的层来学习热图生成。我们首先考虑在获得最低分辨率特征图后生成多分辨率热图的架构。我们的第二种方法允许在特征提取过程中学习，其中在特征提取器的每个分辨率下生成热图。第一种和第二种方法分别称为多分辨率热图学习和多分辨率特征图。我们的体系结构简单而有效，可以实现良好的性能。我们对两个共同的基准进行了实验姿势估计的实验：MSCOCO和MPII数据集。该代码可在https://github.com/tqtrunghnvn/simmrpose上公开获得。
Human pose estimation - the process of recognizing human keypoints in a given image - is one of the most important tasks in computer vision and has a wide range of applications including movement diagnostics, surveillance, or self-driving vehicle. The accuracy of human keypoint prediction is increasingly improved thanks to the burgeoning development of deep learning. Most existing methods solved human pose estimation by generating heatmaps in which the ith heatmap indicates the location confidence of the ith keypoint. In this paper, we introduce novel network structures referred to as multi-resolution representation learning for human keypoint prediction. At different resolutions in the learning process, our networks branch off and use extra layers to learn heatmap generation. We firstly consider the architectures for generating the multi-resolution heatmaps after obtaining the lowest-resolution feature maps. Our second approach allows learning during the process of feature extraction in which the heatmaps are generated at each resolution of the feature extractor. The first and second approaches are referred to as multi-resolution heatmap learning and multi-resolution feature map learning respectively. Our architectures are simple yet effective, achieving good performance. We conducted experiments on two common benchmarks for human pose estimation: MSCOCO and MPII dataset. The code is made publicly available at https://github.com/tqtrunghnvn/SimMRPose.
