# Human Pose Estimation Based on Lightweight Multi-Scale Coordinate Attention
## 1 Introduction  
Key point estimation tasks, which have evolved to be among the most difficult and intensively researched fundamental challenges in the area of computer vision, include tasks like human posture estimation as a typical class of sub-tasks. At the same time, as preprocessing work to understand the challenges of human behavior, human pose estimation is crucial in many different application sectors, including behavior recognition, human-computer interaction, autonomous driving, and video enhancement. Due to the rapid advancement of deep convolution neural networks (CNN), numerous great enhanced algorithm frameworks in the field of two-dimensional multi-person pose estimation have appeared in recent years [1]. [PERSON] et al. [2] carried out a comprehensive survey of human pose estimation in monocular images, including landmark work and recent progress. [PERSON] et al. [3] analyzed the OpenPose, BlazePose, DeepPose, and Hourglass architectures and implemented OpenPose and BlazePose models to locate landmarks on the human push-up posture. [PERSON] et al. [4] systematically summarized the differences and connections between several methods in recent years and further analyzed the solutions of some challenging cases. For example, [PERSON] et al. [5] proposed a graded structured pose representation (SPR), which unifies the person instance and position information. [PERSON] et al. [6] proposed a part-based branching network (PBN) for part of their model to learn the specific representation of each group instead of predicting all joint heatmaps for a branch. Then, the model divided the data-driven part groups by estimating the mutual information of the joints. A fast posture distillation training strategy was used by [PERSON] et al. [7] to trainthe complete teacher hourglass network using a lightweight variation of the hourglass network [8]. [PERSON] et al. [9] introduced a regularization method for information loss by dropping the appearance information of key points with a certain probability during the training process in order to avoid only fitting the appearance information and ignoring the constraint information during the training process. By using transformer's multi-head attention mechanism, [PERSON] et al. [10] minimized the location relationship of network learning and the correlation between several critical points, drastically lowering the number of parameters and model calculations [11]. However, most of the above methods cannot avoid heatmap operation and often require a variety of complex post-processing to improve network accuracy. Numerous helpful ideas have also emerged in a variety of fields for the further development of neural networks. The application of CNN for feature extraction and fusion at various scales for the interaction problem in automated driving was mentioned by [PERSON] et al. [12]. [PERSON] et al. [13] used an attention mechanism to improve the accuracy of the network in the identification of traffic signs. In this paper, we took the Yolo-Pose [14] framework as inspiration to study human pose estimation without heatmaps. Since the problems of multi-scale, occlusion, and illumination problems in object detection tasks are consistent with those in human pose estimation tasks, a good framework to solve the above problems in object detection tasks should also be able to handle similar issues in the field of pose estimation. Therefore, based on the above research, we present an end-to-end, lightweight human pose estimation network based on multi-scale coordinate attention based on Yolo-Pose. The contributions of this paper are as follows:  
1. First, the ghost bottleneck module in the lightweight network GhostNet built for tiny CNN networks was used to replace the common convolution layer in the original model in order to output more flexible and effective feature map information with fewer computing resources. 2.