构建首个公开婴儿呼吸估计数据集，解决小样本难题
Overcoming Small Data Limitations in Video-Based Infant Respiration Estimation
- 基于婴儿特定区域检测与光流增强的时空神经网络
- 在400段视频上建立首个可复现的婴儿呼吸估计基准
- 适合医疗视觉、新生儿监护方向研究者参考
无接触式婴儿呼吸监测有望推动呼吸异常的早期发现与治疗，与神经发育障碍及婴儿猝死综合征（SIDS）相关。尽管成人呼吸估计已有成熟的计算机视觉算法和视频数据集，但目前仅有一个小型公开婴儿视频数据集含呼吸标注，且缺乏可复现的有效算法。本文发布包含400段视频的标注婴儿呼吸数据集（AIR-400），新增275段来自10名受试者的精细标注视频。提出首个可复现的婴儿呼吸估计流程，结合婴儿特异性感兴趣区域检测与光流增强的时空神经网络处理。通过全面实验建立当前视觉方法在婴儿呼吸估计上的首个可复现基准。数据集、代码仓库及训练模型均开源。
原文摘要 · Abstract (English)
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.
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