Real-time multitarget fall detection based on OpenPose

Nan Li; Yu Wang; Fangyu Liu; Wei Huang

doi:10.1117/12.3038877

7 August 2024 Real-time multitarget fall detection based on OpenPose

Nan Li, Yu Wang, Fangyu Liu, Wei Huang

Proceedings Volume 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024); 132292Z (2024) https://doi.org/10.1117/12.3038877
Event: Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 2024, Nanchang, China

Abstract

The frequent occurrence of falls among the elderly has attracted the attention of numerous research institutions and scholars at home and abroad as an important obstacle on the path to a healthy global aging population. Injuries associated with falls pose a great challenge to the elderly in maintaining the normal function of the organism, which seriously threatens the safety of the elderly's lives. Therefore, real-time fall detection based on deep learning is crucial for the health monitoring and rescue of the elderly. To address the problems of large model size, poor timeliness, and inability to accurately recognize multi-target poses in existing pose recognition methods, a lightweight and improved OpenPose real-time fall detection algorithm is proposed to replace the VGG-19 feature extraction network with a lightweight MobileNet network. The experimental results show that the proposed algorithm effectively reduces the model size and computational volume, and has good real-time performance and robustness, which meets the application requirements of real-time multi-person pose detection.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Nan Li, Yu Wang, Fangyu Liu, and Wei Huang "Real-time multitarget fall detection based on OpenPose", Proc. SPIE 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 132292Z (7 August 2024); https://doi.org/10.1117/12.3038877

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
5 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Detection and tracking algorithms

Convolution

Target detection

Feature extraction

Sensors

Tunable filters

Data modeling

Show All Keywords

Keywords/Phrases

Search In:

Publication Years