# CREBAS: Computer-Based REBA Evaluation System for Wood Manufacturers Using MediaPipe
## 2 Backgrounds
### Posture Estimation Techniques
The top-down method is more accurate than the bottom-up method, but it is slower because it first detects the area of the person in the image, and then estimates the person's posture within their bounding box of. The bottom-up method is less accurate than the top-down method, because it first detects the joints in the image and analyzes the joints' correlations to estimate the posture by connecting them. However, it has the advantage of being fast, because there is no human body tracking process. Figure 3 shows the system architecture of OpenPose [20]. OpenPose lacks in accuracy due to this bottom-up method, although its estimation speed is fast. When REBA evaluation is performed using OpenPose, it forces the image to be changed to 432 \(\times\) 368 during the preprocessing, so it could be deformed in the vertical image input, and overlapping or occluded areas could be unmeasured [21]. MediaPipe, another posture estimation technology, is an AI framework provided by Google. It is an open-source cross-platform framework that provides various vision AI functions using various types of perceptual data such as video and audio in the form of a pipeline. This enables a person's face, body parts, and fingers to be estimated in real time. To implement the posture estimation function, which is the key point of this study, requires a model that can know the body skeletal coordinates. The investigation of the characteristics of the above models: they are top-down method, they are light enough to enable real-time inference even with CPU operation, and they can be used for commercial purposes as well. In the posture estimation process, if the user's posture is complicated or occlusion areas occur, the position of the hidden joint needs to be estimated. There are two methods for this: heatmap and regression. The heatmap method is to estimate the position of joints by generating a heatmap for each joint and modifying the offset for each joint. The heatmap method probabilistically calculates the location of major joints among body parts through a learning model, and estimates the position of the most frequent parts in the  
Figure 3: OpenPose Architecture. When the image of (**a**) is input, it detects the joint using the VGG-19 model, one of the CNN models, and creates the heatmap of (**b**). Based on the heatmap, the PAF of (**c**) is constructed for the correlation analysis of joints, and the joints are properly divided and connected as shown in (**d**) to obtain the result of (**e**). form of the heatmap (Figure 4a). Although it has the advantage of being able to recognize the postures of several people, it takes a long time to calculate due to the large amount of computation. On the other hand, the regression model (Figure 4b) requires few computing resources and is highly scalable, but it is vulnerable to occlusion. MidPipe uses a mixture of these two methods to provide a high-accuracy posture estimation with small amount of computation. MediaPipe's pipeline (Figure 5) consists of a body detector and a pose tracker. First, the body area is detected through the body detector, the coordinate of the key-point, and whether there is a person, and the Region of Interest (ROI) of the person is estimated through the posture tracker in the body area. When it is determined that there is a person in the image, only the tracker operates, and when it is determined that there is no person, the detector operates again to detect the body area [22]. Recently, object detection technologies perform the post-processing with Non-Maximum Suppression (NMS). However, NMS can cause malfunctions in complex gestures with overlapping key points, such as hugging or shaking hands. In order to make up for this shortcoming, MediaPipe uses a Face Detector as shown in Figure 6 to detect a face in the image and track the posture based on the face. The face can be quickly estimated because it is the most distinctive among the body parts and has little variation.