Neural networks have improved the accuracy of human pose estimation from a single RGB image. However, such estimation remains difficult, especially when the human body is only partially visible due to a limited field of view of the camera or occlusions. In this paper, we introduce a data augmentation method called body-cropping augmentation (BCA), which generalizes the dataset for effective training in human pose estimation. This technique includes the policies of data generation and the training strategy using the augmented data. The experiments with the COCO val2017 dataset with ground-truth bounding boxes show BCA consistently enhances accuracies of state-of-the-art neural networks by an average of 1.08% without any modification to the network architecture. Moreover, the proposed BCA technique effectively reduces the false negatives of localizing keypoints, especially in an input image with a few visible keypoints.

神经网络提高了从单个RGB图像进行人体姿势估计的准确性。但是，这样的估计仍然很困难，特别是当由于摄像机或遮挡物的视野有限而仅部分可见人体时。在本文中，我们介绍了一种称为身体裁剪增强（BCA）的数据增强方法，该方法概括了用于有效训练人体姿势估计的数据集。该技术包括数据生成策略和使用增强数据的训练策略。COCO val2017的实验带有真实边界框的数据集显示，BCA在不对网络体系结构进行任何修改的情况下，将先进的神经网络的准确性平均提高了1.08％。而且，所提出的BCA技术有效地减少了定位关键点的假阴性，特别是在具有几个可见关键点的输入图像中。