# 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
#### 2.1.3 Recent Human Pose Estimation Methods  
HPE methods are typically classified as either top-down or bottom-up. Top-down HPE models process each individual independently, which generally results in superior keypoint separation and pose estimation accuracy compared to bottom-up approaches. However, their performance drops sharply in complex scenes due to high dependency on its detector. ViTPose [17] proposes a simple baseline that operates on a pure vision transformer for pose estimation tasks. The study follows a common top-down pipeline and focuses on simplicity, scalability, flexibility, and transferability. ViTPose is evaluated on public datasets and shows improved performance compared to existing CNN-based models. These studies did not analyze the results of the model's handling of samples with occlusions and noted that there was no special design for handling occlusions. The TransPose [19] model adopts a top-down pipeline and obtains pose estimation results considering the positional relationship of joints by capturing long-distance spatial relationships based on the integration of transformer encoders and CNNs. These studies simply analyze how the proposed model handles self-occlusion cases of single-person images but do not provide results or analysis on body-to-body occlusion of multi-person images. RTMPose [18] adopted a top-down pose estimation method and introduced a method to balance speed and accuracy to meet the needs of industrial applications. RTMPose uses CSPNeXt [33] as a backbone and a SimCC-based algorithm [34] as a head. In addition, it analyzed the factors affecting performance and latency in existing 2D multi-person pose estimation and applied various techniques based on empirical analysis. Accordingly, the model is designed to preserve accuracy and have low computational complexity. The RTMPose focuses on the work to improve the speed of the pose estimation model but does not address the occlusion problem of pose estimation. SEFD [35] mitigates the challenges posed by occlusions by explicitly incorporating body edge information. The model training adopts a knowledge distillation strategy, wherein a teacher model exploits the predefined SMPL 3D human mesh, and a student model leverages edge cues extracted via classical edge detectors such as the Canny Edge Detector. SEFD substantially enhances both 2D and 3D pose estimation performance, particularly in the presence of complex poses and severe occlusions. BBox-Mask-Pose [36] addresses the issue of performance degradation in top-down human pose estimation caused by overlapping bounding boxes. By utilizing instance segmentation masks generated by object detectors, the model achieves a more precise delineation of boundaries between adjacent individuals. Additionally, it proposes an integrated architecture that jointly optimizes detection, segmentation, and pose estimationthrough iterative refinement. Although this approach substantially mitigates occlusion-related challenges, its reliance on repeated inference steps for high accuracy leads to increased computational complexity. Nevertheless, top-down models tend to have slow inference speeds because pose estimation must be performed repeatedly for each person in the image. Therefore, the top-down models are hard to apply in real-time systems. In contrast to top-down methods, bottom-up HPE models first detect all keypoints in an image and then group them into individuals, which often leads to faster inference and better scalability in crowded scenes. Therefore, top-down HPE models are suitable for real-time applications. OpenPose and HigherHRNet are representative bottom-up models. [PERSON] [37] introduced a Disentangled Waterfall Atrous Spatial Pooling (D-WASP) module to independently capture multi-scale features for each individual part. The D-WASP module refines multi-scale feature maps by employing a waterfall architecture in conjunction with atrous convolutions of diverse dilation rates. This enhanced joint localization accuracy, achieving robust performance in highly crowded environments.