Enhanced Human Action Recognition Using Fusion of Skeletal Joint Dynamics and Structural Features
Joint Authors
Muralikrishna, S. N.
Muniyal, Balachandra
Acharya, U. Dinesh
Holla, Raghurama
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-01
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Abstract EN
In this research work, we propose a method for human action recognition based on the combination of structural and temporal features.
The pose sequence in the video is considered to identify the action type.
The structural variation features are obtained by detecting the angle made between the joints during the action, where the angle binning is performed using multiple thresholds.
The displacement vector of joint locations is used to compute the temporal features.
The structural variation features and the temporal variation features are fused using a neural network to perform action classification.
We conducted the experiments on different categories of datasets, namely, KTH, UTKinect, and MSR Action3D datasets.
The experimental results exhibit the superiority of the proposed method over some of the existing state-of-the-art techniques.
American Psychological Association (APA)
Muralikrishna, S. N.& Muniyal, Balachandra& Acharya, U. Dinesh& Holla, Raghurama. 2020. Enhanced Human Action Recognition Using Fusion of Skeletal Joint Dynamics and Structural Features. Journal of Robotics،Vol. 2020, no. 2020, pp.1-16.
https://search.emarefa.net/detail/BIM-1190206
Modern Language Association (MLA)
Muralikrishna, S. N.…[et al.]. Enhanced Human Action Recognition Using Fusion of Skeletal Joint Dynamics and Structural Features. Journal of Robotics No. 2020 (2020), pp.1-16.
https://search.emarefa.net/detail/BIM-1190206
American Medical Association (AMA)
Muralikrishna, S. N.& Muniyal, Balachandra& Acharya, U. Dinesh& Holla, Raghurama. Enhanced Human Action Recognition Using Fusion of Skeletal Joint Dynamics and Structural Features. Journal of Robotics. 2020. Vol. 2020, no. 2020, pp.1-16.
https://search.emarefa.net/detail/BIM-1190206
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1190206