Open Access2D Pose Estimation Based on Deep Learning for Mobile System
Author(s) -
Yan Liu,
Jingwen Wang,
Yujie Li,
Guangwei Li,
Gaopeng Tang
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2216/1/012081
Subject(s) - pose , computer science , android (operating system) , deep learning , artificial intelligence , mobile phone , mobile device , machine learning , human–computer interaction , real time computing , computer vision , world wide web , telecommunications , operating system
Pose estimation is the detection of human action poses, which is an important task of computer vision. The requirements of tasks such as unmanned driving and intelligent surveillance have also contributed to the development of human pose estimation. In this context, deep learning (DL) has a remarkable impact on human pose estimation. In this study, we investigate a pose estimation algorithm called convolutional pose machines (CPM) and implement it on a mobile platform (i.e., Android). By combining the classical CPM and the mobile deep learning framework Mobile AI Compute Engine (MACE), the model is deployed to the mobile platform to estimate the human pose directly and locally on the mobile phone without relying on the Internet. Thus, the proposed method can achieve effective performance while protecting users’ personal data. Experimental results on real-world data validate that this system has achieved the expected results of accurate recognition of common actions with a simple interface.