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Detecting Complex 3D Human Motions with Body Model Low-Rank Representation for Real-Time Smart Activity Monitoring System
Air Univ, Islamabad, Pakistan.
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology.
Kumoh Natl Inst Technol, Yanghodong, South Korea.
2018 (English)In: KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, ISSN 1976-7277, Vol. 12, no 3, p. 1189-1204Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2018. Vol. 12, no 3, p. 1189-1204
Keywords [en]
3D human pose estimation, skeleton model, real time system, smart home
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:miun:diva-33582DOI: 10.3837/tiis.2018.03.012ISI: 000428948300012Scopus ID: 2-s2.0-85044820861OAI: oai:DiVA.org:miun-33582DiVA, id: diva2:1204415
Available from: 2018-05-08 Created: 2018-05-08 Last updated: 2018-07-06Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
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  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf