Effect of Compressed Sensing Rates and Video Resolutions on a PoseNet Model in an AIoT System

Abstract

To provide an artificial intelligence service such as pose estimation with a PoseNet model in an Artificial Intelligence of Things (AIoT) system, an Internet of Things (IoT) sensing device sends a large amount of data such as images or videos to an AIoT edge server. This causes serious data traffic problems in IoT networks. To mitigate these problems, we can apply compressed sensing (CS) to the IoT sensing device. However, the AIoT edge server may have poor pose estimation accuracy (i.e., pose score), because it has to recover the CS data received from the IoT sensing device and estimate human pose from the imperfectly recovered data according to CS rates. Therefore, in this paper, we analyze the effect of CS rates (from 100% to 10%) and video resolutions (1280×720, 640×480, 480×360) in the IoT sensing device on the pose score of the PoseNet model in the AIoT edge server. When only considering the meaningful range of CS rates from 100% to 50%, we found that the higher the video resolution, the lower the pose score. At the CS rate of 80%, we could reduce data traffic by 20% despite the degradation in pose score of less than about 0.03 for all video resolutions.

Keywords

• Internet of Things
• data traffic
• compressed sensing
• deep learning
• edge computing
• MobileNet v1