# Effect of Compressed Sensing Rates and Video Resolutions on a PoseNet Model in an AIoT System
## 3 Experimental Results  
We describe experimental results to analyze the effect of CS rates and video resolutions on the PoseNet model in the proposed AIoT system. First, the IoT-sensing device was developed using a Raspberry Pi 4 Model B equipped with a C270 HD webcam to extract and compress real-time videos at 30 frames per second (fps). Its IoT client application was implemented by modifying an open-source IoT device platform, called &Cube-Thyme. For the convenience of the experiment, we used three 50-second video clips [43] with \(1280\times 720\), \(640\times 480\), and \(480\times 320\) resolutions pre-recorded by the IoT-sensing device. We used the same video extracted at three video resolutions, the network input size of the PoseNet model. Next, the AIoT edge server was developed by using a laptop equipped with a Google Coral USB Accelerator, where we installed PyCoral, TensorFlow Lite Runtime, and libedgetpu-max [44]. Its IoT edge middleware was implemented with an open source IoT server platform, called Mobius. Figures 3-5 show the outputs of the PoseNet model for original and recovered images according to CS rates and video resolutions. It should be noted that the recovered images are typically contaminated by recovery errors, measured for various values of the CS rate. In Figure 3, the outputs of the PoseNet model are represented according to the CS rates when the input video resolution is \(1280\times 720\). Compared with Figure 3a, Figure 3b,d indicate that the PoseNet model similarly detects keypoints of human instances but misconcencts some keypoints due to the recovery errors. The number of estimated human poses is slightly reduced in Figure 3e, whereas it is significantly reduced in Figure 3f, where only a few keypoints are detected. In Figure 4, the outputs of the PoseNet model are represented according to the CS rates when the input video resolution is \(640\times 480\). Comparing Figure 4a with Figure 3a, the PoseNet model can estimate the human pose of the largest human instance as well as that of the human instance climbing stairs. As shown in Figure 4d,e, it can detect and connect some keypoints of the largest human instance. In Figure 4f, it can not detect any keypoint of human instances due to severe recovery errors. In Figure 5, the outputs of the PoseNet model are represented according to the CS rates when the input video resolution is \(480\times 360\). Figure 5b,c show that most keypoints of the largest human instance are detected, but a few detected keypoints are disconnected. The number of estimated human poses in Figure 5e is lower than the number of those in Figure 4e. As in Figure 4f, no keypoint of human instances is detected in Figure 5f. In Figure 6, pose scores of the PoseNet model are shown according to CS rates and video resolutions, where the pose score denotes the overall confidence in pose estimation and ranges between 0.0 and 1.0. The pose score is the average pose score for the entire frame of each video. Regardless of video resolution, the pose score gradually decreased as the CS rate decreased from 100% to 50%, but sharply decreased as the CS rate decreased from 50% to 10%. In addition, when the CS rate ranged from 100% to 50%, the PoseNet model showed better pose scores at lower video resolutions. At the CS rate of 80%, we could reduce the data traffic by 20%, but the pose score slightly decreased at all video resolutions. At the CS rate of 70%, the data traffic was reduced by 30%, even though each pose score for video resolutions of 1280 \(\times\) 720, 640 \(\times\) 480, and 480 \(\times\) 360 decreased by 0.0208, 0.0467, and 0.0539, respectively. At the CS rate of 60%, we could reduce the data traffic by 40%, but the pose scores for video resolutions of 640 \(\times\) 480, and 480 \(\times\) 360 decreased by more than 0.05. Note that when the CS rate was 80% and the video resolution was 1280 \(\times\) 720, we could reduce the data traffic by 20%, and the pose score only decreased by about 0.01.