Comparison of Single-Shot and Two-Shot Deep Neural Network Models for Pose Estimation in Assistance Living Application

Abstract

Estimating human posture from an image or video is an essential task in computer vision. This task has detected body key points from a camera for body posture and gesture recognition technology, which enables the following applications: assisted living in the case of fall detection, yoga pose identification, character animation, and an autonomous drone control system. The rapid development of AI-based posture estimation algorithms for picture recognition has resulted in the availability of quick and dependable solutions for recognizing the human body joint in collected films. One major issue in human posture assessment is the system’s capacity to perform with high accuracy in real-time under shifting ambient conditions. The ultimate goal of the proposed transfer learning-based posture estimation assignment is to achieve real-time speed with virtually no drop accuracy. In this research paper, assisted living program (ALP) is implemented by using a single-shot deep estimation network and a pose key points angular feature. Experimental results show that transfer learning-based pose identifies and estimates posture with a frame rate of about 30 frames per second and a detection accuracy rate of 96.81%.