Automatic markerless estimation of infant posture and motion from ordinary
videos carries great potential for movement studies "in the wild", facilitating
understanding of motor development and massively increasing the chances of
early diagnosis of disorders. There is rapid development of human pose
estimation methods in computer vision thanks to advances in deep learning and
machine learning. However, these methods are trained on datasets featuring
adults in different contexts. This work tests and compares seven popular
methods (AlphaPose, DeepLabCut/DeeperCut, Detectron2, HRNet,
MediaPipe/BlazePose, OpenPose, and ViTPose) on videos of infants in supine
position. Surprisingly, all methods except DeepLabCut and MediaPipe have
competitive performance without additional finetuning, with ViTPose performing
best. Next to standard performance metrics (object keypoint similarity, average
precision and recall), we introduce errors expressed in the neck-mid-hip ratio
and additionally study missed and redundant detections and the reliability of
the internal confidence ratings of the different methods, which are relevant
for downstream tasks. Among the networks with competitive performance, only
AlphaPose could run close to real time (27 fps) on our machine. We provide
documented Docker containers or instructions for all the methods we used, our
analysis scripts, and processed data at this https URL
and this https URL

通过比较七种不同的姿势估计方法，对婴儿在仰卧位的视频进行分析，发现除了 DeepLabCut 和 MediaPipe 外，其他方法在无需微调的情况下都表现出竞争力，其中 ViTPose 表现最佳。同时，引入颈中臀比例的错误和其他误测、多余检测以及不同方法的内部置信度评分可靠性的研究，这些都对下游任务有重要意义。