The development of contactless respiration monitoring for infants could enable advances in the early detection and treatment of breathing irregularities, which are associated with neurodevelopmental impairments and conditions like sudden infant death syndrome (SIDS). But while respiration estimation for adults is supported by a robust ecosystem of computer vision algorithms and video datasets, only one small public video dataset with annotated respiration data for infant subjects exists, and there are no reproducible algorithms which are effective for infants. We introduce the annotated infant respiration dataset of 400 videos (AIR-400), contributing 275 new, carefully annotated videos from 10 recruited subjects to the public corpus. We develop the first reproducible pipelines for infant respiration estimation, based on infant-specific region-of-interest detection and spatiotemporal neural processing enhanced by optical flow inputs. We establish, through comprehensive experiments, the first reproducible benchmarks for the state-of-the-art in vision-based infant respiration estimation. We make our dataset, code repository, and trained models available for public use.

翻译：婴儿无接触呼吸监测技术的发展，有望推动与神经发育障碍及婴儿猝死综合征（SIDS）等疾病相关的呼吸异常早期检测与治疗的进步。然而，成人呼吸估计已有成熟的计算机视觉算法和视频数据集生态支持，而目前仅存在一个包含婴儿呼吸标注的小型公开视频数据集，且尚无针对婴儿的有效可复现算法。我们提出了包含400个视频的标注婴儿呼吸数据集（AIR-400），向公共数据集中新增了来自10名受试者的275个精心标注视频。基于婴儿特异性感兴趣区域检测及光流输入增强的时空神经处理，我们开发了首个可复现的婴儿呼吸估计流程。通过全面实验，我们建立了首个基于视觉的婴儿呼吸估计领域可复现的最先进基准。我们的数据集、代码库及训练模型均已公开供研究使用。