# Effect of Compressed Sensing Rates and Video Resolutions on a PoseNet Model in an AIoT System
## 2 Proposed AIoT System with Compressed Sensing and Pose Estimation
### Pose Estimation with a PoseNet Model  
Pose estimation is the process of determining the configuration of the body from images or videos to estimate the positions of human keypoints, typically human joints, and associate them [40]. This is a highly popular research topic in the field of computer vision, which is widely used in human-computer interactions, gaming, virtual reality, video surveillance, etc. [41]. As mentioned above, we used the PoseNet model for pose estimation [25]. Figure 2 illustrates the PoseNet model with the MobileNet v1 backbone architecture proper for our AIoT edge server. When making pose estimations using the PoseNet model, 17 keypoints were detected, including 5 facial points, such as two eyes, two ears, and one nose, and 12 joints of the body, such as shoulders, elbows, wrists, hips, knees, and ankles. Then, the human pose was estimated by associating the closest two keypoints for human skeleton representation. The MobileNet v1 consists of 18 Conv2D layers and 13 Depthwise Conv2D layers. The first Conv2D layer receives an input image of (\(1\times 481\times 641\times 3\)) as a tensor of (batch \(\times\) height \(\times\) width \(\times\) channels). Then, it outputs a feature map of (\(1\times 241\times 321\times 24\)) with a filter of (\(24\times 3\times 3\times 3\)) and a bias of (24). The second DepthwiseConv2D layer receives the feature map and outputs a new feature map with a weight of (\(1\times 3\times 3\times 24\)) and a bias of (24). The above process is repeated for other Conv2D and DepthwiseConv2D layers. The last Conv2D layer feeds the same feature map of (\(1\times 31\times 41\times 384\)) to keypoint heatmaps, short-range offsets, and mid-range pairwise offsets functions. The keypoint heatmaps function with a Conv2D layer predicts 17 heatmaps of (\(1\times 31\times 41\times 17\)) with a filter of (\(17\times 1\times 384\)) and a bias of (17). During training, the average logistic loss was considered for predicted heatmaps. To improve the keypoint position accuracy, the short-range offsets function with a Conv2D layer predicts short-range offset vectors pointing from the position within the keypoint disks to the keypoint of the closest human instance [25]. During training, the short-range offset prediction errors were penalized with the \(L_{1}\) loss, and only the errors at the position in the keypoint disks were averaged and back-propagated. To connect pairs of keypoints belonging to each human instance, the mid-range pairwise offset function with two Conv2D layers and concatenation predicts separate pairwise mid-range 2D offset vectors, which are target regression vectors for each directed edge, connecting pairs of keypoints that are adjacent to each other in a tree-structured kinematic graph of the person. During training, the average \(L_{1}\) loss of the regression prediction errors over the source keypoint disks was computed and back-propagated through the network. The multi-person pose decoded function group keypoints into detected human instances using Hough score maps and mid-range offset vectors. The Hough score maps can be obtained by aggregating heatmaps and short-range offsets through Hough voting. The pose score, which is the average of keypoint scores, is calculated using Expected-Object Keypoint Similarity (OKS), keypoint heatmaps \(p_{k}(x)\), and the Hough score maps [42]. The detected human instance with key point coordinates \(y_{pj,pk}\) is called \(pj\), and \(\lambda_{p}j\) is  
Figure 2: The PoseNet model with the MobileNet v1 backbone architecture. the square root of the bounding box area, which tightly contains all detected keypoints of the \(pj\)-th person instance.