Affiliation:
1. School of Computing and Data Engineering, NingboTech University, Ningbo 315100, China
Abstract
Modern embedded systems have achieved relatively high processing power. They can be used for edge computing and computer vision, where data are collected and processed locally, without the need for network communication for decision-making and data analysis purposes. Face detection, face recognition, and pose detection algorithms can be executed with acceptable performance on embedded systems and are used for home security and monitoring. However, popular machine learning frameworks, such as MediaPipe, require relatively high usage of CPU while running, even when idle with no subject in the scene. Combined with the still present false detections, this wastes CPU time, elevates the power consumption and overall system temperature, and generates unnecessary data. In this study, a low-cost low-resolution infrared thermal sensor array was used to control the execution of MediaPipe’s pose detection algorithm using single-board computers, which only runs when the thermal camera detects a possible subject in its field of view. A lightweight algorithm with several filtering layers was developed, which allowed the effective detection and isolation of a person in the thermal image. The resulting hybrid computer vision proved effective in reducing the average CPU workload, especially in environments with low activity, almost eliminating MediaPipe’s false detections, and reaching up to 30% power saving in the best-case scenario.
Funder
Ningbo Clinical Research Center for Medical Imaging
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference20 articles.
1. (2023, April 22). Google MediaPipe. Available online: https://developers.google.com/mediapipe.
2. (2023, March 03). Ultralytics YOLOv8 Docs. Available online: https://docs.ultralytics.com/tasks/pose/.
3. OpenPose (2023, March 03). OpenPose. Available online: https://cmu-perceptual-computing-lab.github.io/openpose/web/html/doc/index.html.
4. Toshev, A., and Szegedy, C. (2014, January 23–28). DeepPose: Human Pose Estimation via Deep Neural Networks. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.
5. Cao, Z., Simon, T., Wei, S.-E., and Sheikh, Y. (2017, January 21–26). Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.