Two-Dimensional Ultra Light-Weight Infant Pose Estimation with Single Branch Network

Motivated by the increasing interest in clinical studies focused on infant movements and poses, this research addresses the limited emphasis on speed and efficiency in existing 2D and 3D pose estimation methods, particularly concerning infant datasets. The scarcity of publicly available infant data poses a significant challenge. In response, we aim to develop a lightweight pose estimation model tailored for edge devices and CPUs. Drawing inspiration from the OpenPose-2016 approach, we refine the algorithm’s architecture, focusing on 2D image training. The resulting model, with 4.09 million parameters, features a single-branch structure. During execution, it achieves an algorithmic complexity of 8.97 giga floating-point operations per second (GFLOPS), enabling operation at approximately 23 frames per second on a Core i5-10400f processor.Notably, this approach balances compact dimensions with superior performance on our self-collected infant dataset. We anticipate that this pragmatic methodology establishes a robust foundation, addressing the need for speed and efficiency in infant pose estimation and providing favorable conditions for future research in this application.