Detecting Complex 3D Human Motions with Body Model Low-Rank Representation for Real-Time Smart Activity Monitoring System
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jalal, Ahmad | - |
dc.contributor.author | Kamal, Shaharyar | - |
dc.contributor.author | Kim, Dong-Seong | - |
dc.date.available | 2020-04-24T10:25:39Z | - |
dc.date.created | 2020-03-31 | - |
dc.date.issued | 2018-03-31 | - |
dc.identifier.issn | 1976-7277 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/342 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | KSII-KOR SOC INTERNET INFORMATION | - |
dc.subject | HUMAN ACTIVITY RECOGNITION | - |
dc.subject | DEPTH SILHOUETTES | - |
dc.subject | FEATURES | - |
dc.subject | TRACKING | - |
dc.subject | HMM | - |
dc.title | Detecting Complex 3D Human Motions with Body Model Low-Rank Representation for Real-Time Smart Activity Monitoring System | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Dong-Seong | - |
dc.identifier.doi | 10.3837/tiis.2018.03.012 | - |
dc.identifier.scopusid | 2-s2.0-85044820861 | - |
dc.identifier.wosid | 000428948300012 | - |
dc.identifier.bibliographicCitation | KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, v.12, no.3, pp.1189 - 1204 | - |
dc.citation.title | KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS | - |
dc.citation.volume | 12 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 1189 | - |
dc.citation.endPage | 1204 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.subject.keywordPlus | HUMAN ACTIVITY RECOGNITION | - |
dc.subject.keywordPlus | DEPTH SILHOUETTES | - |
dc.subject.keywordPlus | FEATURES | - |
dc.subject.keywordPlus | TRACKING | - |
dc.subject.keywordPlus | HMM | - |
dc.subject.keywordAuthor | 3D human pose estimation | - |
dc.subject.keywordAuthor | skeleton model | - |
dc.subject.keywordAuthor | real time system | - |
dc.subject.keywordAuthor | smart home | - |
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