Detecting Complex 3D Human Motions with Body Model Low-Rank Representation for Real-Time Smart Activity Monitoring System
- Authors
- Jalal, Ahmad; Kamal, Shaharyar; Kim, Dong-Seong
- Issue Date
- 31-Mar-2018
- Publisher
- KSII-KOR SOC INTERNET INFORMATION
- Keywords
- 3D human pose estimation; skeleton model; real time system; smart home
- Citation
- KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, v.12, no.3, pp.1189 - 1204
- Journal Title
- KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS
- Volume
- 12
- Number
- 3
- Start Page
- 1189
- End Page
- 1204
- URI
- https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/342
- DOI
- 10.3837/tiis.2018.03.012
- ISSN
- 1976-7277
- Abstract
- Detecting and capturing 3D human structures from the intensity-based image sequences is an inherently arguable problem, which attracted attention of several researchers especially in real-time activity recognition (Real-AR). These Real-AR systems have been significantly enhanced by using depth intensity sensors that gives maximum information, in spite of the fact that conventional Real-AR systems are using RGB video sensors. This study proposed a depth-based routine-logging Real-AR system to identify the daily human activity routines and to make these surroundings an intelligent living space. Our real-time routine-logging Real-AR system is categorized into two categories. The data collection with the use of a depth camera, feature extraction based on joint information and training/recognition of each activity. In-addition, the recognition mechanism locates, and pinpoints the learned activities and induces routine-logs. The evaluation applied on the depth datasets (self-annotated and MSRAction3D datasets) demonstrated that proposed system can achieve better recognition rates and robust as compare to state-of-the-art methods. Our Real-AR should be feasibly accessible and permanently used in behavior monitoring applications, humanoid-robot systems and e-medical therapy systems.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - School of Electronic Engineering > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/342)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.