# Two-Dimensional Ultra Light-Weight Infant Pose Estimation with Single Branch Network
## 1 Introduction
The fastest method with high accuracy was developed in [10]; the authors reported this method running at 23 fps with a graphics card for images of three people, and if the number of people increases to 20, it will only run at 15 frames per second. However, bottom-up approach of Openpose can run stably and immutably when running with a variable number of people. Therefore, we prioritize using the bottom-up approach and improve the model of Openpose to be faster and lighter. _Infant Pose Estimation:_ Presently, approaches to analyzing, recognizing, and clinically researching infant posture heavily rely on visual observation by experts, either in real-time or through recorded sessions [11]. There appear significant differences in body proportions and pose flexibility between infants and adults, creating significant challenges for pose estimation models. Most existing models, often trained on adult datasets due to accessibility, do not exhibit optimal performance on infant datasets. Compounding this issue is the limited availability of infant data, often withheld for confidentiality reasons, posing a major obstacle to training models from scratch. Consequently, only a handful of research efforts have ventured into automating postural monitoring of infants in recorded videos. In a notable attempt to address this challenge, a proposal was presented in [12] to automatically extract key points of infants. The author employed the OpenPose-2019 method [13] for this purpose. However, the accuracy of this approach was compromised due to the scarcity of databases and the inherent complexity of infant movements. A contrasting approach is exemplified by AggPose [14], which boasts high efficiency with minimal computational overhead, adopting a top-down strategy. Nonetheless, it is acknowledged that the top-down approach may exhibit reduced stability compared to the bottom-up approach, especially in scenarios involving multiple objects within the image. The pursuit of an accurate and efficient solution for infant pose estimation remains a dynamic area of research, balancing the intricacies of infant movements with the limitations of available datasets and computational methodologies. Navigating the intricate trade-offs between performance and size while optimizing the complexity and parameters of a model poses a challenging endeavor. The study conducted by [15] stands as a noteworthy example where computational costs were successfully curtailed and performance was enhanced. This was achieved through a strategic alteration in the architecture, specifically replacing the original model's 5 \(\times\) 5 and 7 \(\times\) 7 convolutional layers with two overlapping 3 \(\times\) 3 layers. This substitution not only streamlined computational expenses but also yielded an augmentation in the overall efficacy of the method. Building on this paradigm, the techniques outlined in [16] present a broader perspective on improvement methodologies across functions. One consistent approach highlighted in this study involves the systematic replacement of large convolutional layers with overlapping smaller layers, thereby contributing to enhanced efficiency and optimized model performance. Despite the increasing amount of research related to human pose estimation and real-time improvement in running, current methods have not achieved high accuracy for the infant dataset. Current research has been exclusively conducted on adult datasets, whereas there is a significant difference in body proportions between infants and adults. There need to be multiple foundational components to determine a virtual skeleton (pose estimation/body part localization), such as human object detection, body joint detection, and establishing relationships between the joints. Therefore, current methods often utilize multiple branches or at least two independent branches to determine separate foundations and create a complete framework of a human subject. This paper introduces a new definition of the single-branch model for infant pose estimation.