Vision-based human activity recognition using LDCRFs
Author
Source
The International Arab Journal of Information Technology
Issue
Vol. 15, Issue 3 (31 May. 2018)7 p.
Publisher
Publication Date
2018-05-31
Country of Publication
Jordan
No. of Pages
7
Main Subjects
Information Technology and Computer Science
Abstract EN
In this paper, an innovative approach for human activity relies on affine-invariant shape descriptors and motion flow is proposed.
The first phase of this approach is to employ the modelling background that uses an adaptive Gaussian mixture to distinguish moving foregrounds from their moving cast shadows.
Accordingly, the extracted features are derived from 3D spatio-temporal action volume like elliptic Fourier, Zernike moments, mass center and optical flow.
Finally, the discriminative model of Latent-dynamic Conditional Random Fields (LCDRFs) performs the training and testing action processes using the combined features that conforms vigorous view-invariant task.
Our experiment on an action Weizmann dataset demonstrates that the proposed approach is robust and more efficient to problematic phenomena than previously reported.
It also can take place with no sacrificing real-time performance for many practical action applications.
American Psychological Association (APA)
al-Muzayyan, Mahmud. 2018. Vision-based human activity recognition using LDCRFs. The International Arab Journal of Information Technology،Vol. 15, no. 3.
https://search.emarefa.net/detail/BIM-839269
Modern Language Association (MLA)
al-Muzayyan, Mahmud. Vision-based human activity recognition using LDCRFs. The International Arab Journal of Information Technology Vol. 15, no. 3 (May. 2018).
https://search.emarefa.net/detail/BIM-839269
American Medical Association (AMA)
al-Muzayyan, Mahmud. Vision-based human activity recognition using LDCRFs. The International Arab Journal of Information Technology. 2018. Vol. 15, no. 3.
https://search.emarefa.net/detail/BIM-839269
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-839269