# Human Pose Estimation Based on Lightweight Multi-Scale Coordinate Attention
## 2 Related Work  
Existing deep learning-based, two-dimensional, multi-person pose estimation methods can be divided into two types: two-stage and one-stage methods. In the two-stage approach, based on the starting point of the prediction, there are two types of strategies: top-down and bottom-up strategies.  
Top-down approaches require first locating the human body and then estimating the individual pose within the corresponding bounding box area. G-RMI was proposed by [PERSON] et al. [15], who used a unique form of confidence estimation based on key points in place of scoring based on a target box and a new non-maximum suppression (NMS) based on key points in place of the coarser NMS based on the human body. To lessen network redundancy, [PERSON] et al. [16] used the symmetric spatial transformer network (SSTN) and parametric pose non-maximum suppression (PNMS). The CPN method was proposed by [PERSON] et al. [17], who also included the OHKM (online hard key points mining) approach. These authors used GlobalNet and RefineNet, respectively, for various key points. An innovative high-resolution network with multi-scale feature fusion was proposed by [PERSON] et al. [18], which considerably improved the precision of human pose estimates. Top-down approaches are popular due to their high accuracy, but they are still constrained by the complexity of the detection network, which does not support the expansion and implementation of real-time tasks.  
Bottom-up approaches detect identity-free key points in images and then divide them into individuals via grouping operations. To create a bigger receptive field using a large convolution kernel, [PERSON] et al. [19] introduced the OpenPose approach. They also suggested that part affinity fields (PAFs) increased the grouping speed of human important spots. Then, they put forward the PeronLab [20] approach and a novel circular method, which significantly enhanced the predictability of two distant keypoints. Part association field (PAF) and part intensity field (PIF) were proposed by [PERSON] et al. [21] to map the locations of human joint points and the connections between them. In order to lower the computational burden, [PERSON] et al. [22] incorporated an effective deconvolution module into the high-resolution network (HRNet) and developed a higher resolution network (HigherHRNet), which included multi-resolution training and a heatmap aggregation technique in the training stage. To generate the convolution kernel standard deviation for each human key point, [PERSON] et al. [23] developed the scale adaptive heatmap regression (SAHR) approach, and they proposed the weighted adaptive heatmap regression (WAHR) method to balance the foreground and background. Bottom-up approaches run stably and quickly, but they rely on heatmaps and complex post-processing grouping algorithms, resulting in unavoidable quantification errors.  
For single-shot methods, in order to achieve the correlation and feature fusion of context information between channels, [PERSON] et al. [24] incorporated the channel shuffle module (CSM) and attention mechanism into the network. [PERSON] et al. [25] used FCOS for multi-person attitude estimation and used CondConv to generate different convolution kernel parameters for different human instances. Single-stage methods can directly output the location of human key points, thus avoiding grouping operations, but they often do not perform as well as top-down methods and require additional post-processing steps to improve accuracy.  
To further prevent the key point detection network's complexity and duplicate post-processing activity brought on by heatmaps, this paper was committed to enhancing the human pose estimate method without heatmaps. We propose a lightweight human pose estimation network based on multi-scale coordinate attention, which integrates the lightweight GhostNet module [26] and adds the coordinate attention mechanism. BiFPN is employed concurrently to achieve bidirectional, multi-scale feature fusion.