# Robust Human Pose Estimation Method for Body-to-Body Occlusion Using RGB-D Fusion Neural Network
## 2 Related Works
### Human Pose Estimation Methods Based on RGB Images
#### 2.1.4 Feature Extractors for Human Pose Estimation  
CPM [29] extracts features using a shallow stack of convolutional layers at the beginning of the network, focusing on capturing essential image representations before passing  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **Year** & **Backbone** & **Approach** & **Description** \\ \hline [PERSON] et al. [29] & 2016 & CPM & Top-down & Intermediate supervision \\ \hline [PERSON] et al. [30] & 2017 & VGG-19 & Bottom-up & Part Affinity Field \\ \hline [PERSON] et al. [20] & 2020 & HRNet & Bottom-up & Focus on scale change challenges in \\  & & & & bottom-up pipelines \\ \hline [PERSON] et al. [35] & 2023 & ResNet/HRNet & Top-Down & 3D mesh estimation by edge information, Knowledge distillation strategy \\ \hline [PERSON] et al. [36] & 2023 & HRNet & Bottom-up & Waterfall architecture, Capture multi-scale features, \\ \hline [PERSON] et al. [37] & 2023 & HRNet & Bottom-up & A distance-based loss function for joint heatmaps \\ \hline [PERSON] et al. [38] & 2024 & ViTPose & Top-Down & Integration of detection, segmentation, and pose estimation \\ \hline \hline \end{tabular}
\end{table}
Table 1: Recent studies for multi-person human pose estimation.  
them to the multi-stage refinement modules. This approach is effective for integrating both local and contextual information during the subsequent stages. However, the feature representations are less expressive due to the lightweight structure. OpenPose adopted VGG as its feature extractor. VGG [40] consists of a series of convolutional layers with small receptive fields (3 \(\times\) 3 filters) and uses deep stacking of these layers, followed by fully connected layers. This simple and uniform architecture allows VGG to effectively capture hierarchical features from images. While VGG offers an intuitive architecture and demonstrates strong performance on large-scale datasets, its large number of parameters results in slow inference speed. To address this limitation, SimpleBaseline [41] employs ResNet [42] as the backbone. ResNet, with its introduction of residual blocks, enables more efficient computation in deep networks. However, as the network depth increases, the spatial resolution of feature maps decreases, which can lead to errors in joint detection. To overcome this issue, HRNet [31] was proposed. HRNet maintains high-resolution feature maps throughout the network and processes multi-resolution information in parallel, continuously fusing these representations. This design enables more precise keypoint localization, even for small joints or complex poses. Nonetheless, HRNet tends to encounter difficulties when applied to real-time applications due to its high computational and memory requirements. More recently, CSPNeXt [33] has been proposed, which combines the partial connection structure of CSPNet [43] with the NeXt block [44]. This allows for a substantial reduction in parameter count and computational cost while maintaining high performance. As a result, CSPNeXt inherits the advantages of high-resolution networks like HRNet, while also achieving the speed and compactness necessary for real-time and mobile applications.