Human Depth Sensors-Based Activity Recognition Using Spatiotemporal Features and Hidden Markov Model for Smart Environments
Joint Authors
Jalal, Ahmad
Kamal, Shaharyar
Kim, Daijin
Source
Journal of Computer Networks and Communications
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-10-04
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Information Technology and Computer Science
Abstract EN
Nowadays, advancements in depth imaging technologies have made human activity recognition (HAR) reliable without attaching optical markers or any other motion sensors to human body parts.
This study presents a depth imaging-based HAR system to monitor and recognize human activities.
In this work, we proposed spatiotemporal features approach to detect, track, and recognize human silhouettes using a sequence of RGB-D images.
Under our proposed HAR framework, the required procedure includes detection of human depth silhouettes from the raw depth image sequence, removing background noise, and tracking of human silhouettes using frame differentiation constraints of human motion information.
These depth silhouettes extract the spatiotemporal features based on depth sequential history, motion identification, optical flow, and joints information.
Then, these features are processed by principal component analysis for dimension reduction and better feature representation.
Finally, these optimal features are trained and they recognized activity using hidden Markov model.
During experimental results, we demonstrate our proposed approach on three challenging depth videos datasets including IM-DailyDepthActivity, MSRAction3D, and MSRDailyActivity3D.
All experimental results show the superiority of the proposed approach over the state-of-the-art methods.
American Psychological Association (APA)
Jalal, Ahmad& Kamal, Shaharyar& Kim, Daijin. 2016. Human Depth Sensors-Based Activity Recognition Using Spatiotemporal Features and Hidden Markov Model for Smart Environments. Journal of Computer Networks and Communications،Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1107860
Modern Language Association (MLA)
Jalal, Ahmad…[et al.]. Human Depth Sensors-Based Activity Recognition Using Spatiotemporal Features and Hidden Markov Model for Smart Environments. Journal of Computer Networks and Communications No. 2016 (2016), pp.1-11.
https://search.emarefa.net/detail/BIM-1107860
American Medical Association (AMA)
Jalal, Ahmad& Kamal, Shaharyar& Kim, Daijin. Human Depth Sensors-Based Activity Recognition Using Spatiotemporal Features and Hidden Markov Model for Smart Environments. Journal of Computer Networks and Communications. 2016. Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1107860
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1107860