Supine Infant Pose Estimation via Single Depth Image

Abstract

objective: The infant pose estimation is very important for the evaluation of infants’ brain development and motor quality. Here, we propose a new method to quickly build a large-scale depth image pose dataset, and on this basis, establish the XJTU-IDP dataset, then we also propose a new method based on joint feature coding to estimate the infants’ posture from depth image. Methods: First, we use Kinect V2 to collect depth and color images synchronously, and use the proposed framework to build a large dataset. Then we encode the offset vector in the confidence region of each joint of the infant, and export the joint positioning and connection encoding through our convolutional neural network (CNN) model. The proposed method is tested from two datasets (including 16 and 27 clinical actual infants, respectively). Results: When applied to pose estimation, the median root mean square distance (RMSD) calculated among all limbs is 4.12 pixels, which is much lower than the baseline (9.06 pixels) on babyPose dataset and RMSD in XJTU-IDP dataset is 3.93 pixels, which achieves the best in comparison with the state-of-the-arts. Conclusion: The proposed framework of the building dataset in our work is effective, and the joint feature coding can enrich the network information. Significance: Our work not only provides a larger infant pose dataset to make up for the deficiencies of babyPose, but also proposes a new framework for quickly establishing high-quality dataset. The proposed pose estimation method significantly improves the state of art in automatic measurement of infants’ health status.