CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 12599 > Article
Paper
3 April 2023 Indoor person behavior recognition based on deep learning and knowledge graph
Lihong Cheng, Xiantai Gou, Cen Tang, Yuan Li, Xiaofeng Jiang, Hongyu Zuo
Author Affiliations +
Proceedings Volume 12599, Second International Conference on Digital Society and Intelligent Systems (DSInS 2022); 125990D (2023) https://doi.org/10.1117/12.2673563
Event: 2nd International Conference on Digital Society and Intelligent Systems (DSInS 2022), 2022, Chendgu, China
Abstract
At present, indoor human behavior recognition technology is widely used in the fields of video intelligent monitoring, intelligence elderly care and intelligent medical care, etc. The existing research methods are more focused on classifying human behavior and ignore the connection between objects in the scene and human behavior. In order to make full use of the association between the objects in the scene and indoor human behavior, this paper proposes an indoor human behavior recognition model based on deep learning and knowledge graph. Firstly, we use YOLOv5 target detection network to identify the objects in the scene, and also use human keypoint detection algorithm to locate the skeletal keypoints of human body, extract and analyze the spatial features of objects and keypoints to obtain the human behavior feature triad, construct the behavior detection knowledge graph and perform search and inference to realize human behavior recognition. The experimental results show that the recognition accuracy of the model is 94.9% on the homemade test set.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Lihong Cheng, Xiantai Gou, Cen Tang, Yuan Li, Xiaofeng Jiang, and Hongyu Zuo "Indoor person behavior recognition based on deep learning and knowledge graph", Proc. SPIE 12599, Second International Conference on Digital Society and Intelligent Systems (DSInS 2022), 125990D (3 April 2023); https://doi.org/10.1117/12.2673563
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
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
 10 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Object detection
Target recognition
Target detection
Deep learning
Feature extraction
Convolutional neural networks
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top