Abstract
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.
Abstract (translated)
针对婴儿的非接触式呼吸监测的发展可能有助于早期发现和治疗与神经发育障碍及婴儿猝死综合症(SIDS)相关的呼吸不规律。然而，尽管成人呼吸估计有强大的计算机视觉算法和支持视频数据集生态系统，但仅存在一个包含少量带有标注呼吸数据的小型公共视频数据集用于婴儿，并且没有适用于婴儿的有效可重复的算法。我们介绍了注释婴儿呼吸数据集（AIR-400），其中包括从10名招募来的受试者中收集到的275个新的、精心标记的视频，为公众贡献了400个视频的数据集合。我们开发了首个基于特定于婴儿的兴趣区域检测和通过光学流输入增强的空间时间神经处理的第一批可重复管道用于婴儿呼吸估计。通过全面实验，我们建立了第一个基于视觉技术的婴儿呼吸估计的先进状态下的可重复基准。我们将数据集、代码库以及训练模型向公众开放使用。
URL
https://arxiv.org/abs/2512.06888
