Paper Info
Title
Skeleton Based Human Action Recognition For Smart City Application Using Deep Learning
Abstract
These days the Human Action Recognition (HAR) is playing a vital role in several applications such as surveillance systems, gaming, robotics, and so on. Interpreting the actions performed by a person from the video is one of the essential tasks of intelligent surveillance systems in the smart city, smart building, etc. Human action can be recognized either by using models such as depth, skeleton, or combinations of these models. In this paper, we propose the human action recognition system based on the 3D skeleton model. Since the role of different joints varies while performing the action, in the proposed work, we use the most informative distance and the angle between joints in the skeleton model as a feature set. Further, we propose a deep learning framework for human action recognition based on these features. We performed experiments using MSRAction3D, a publicly available dataset for 3D HAR, and the results demonstrated that the proposed framework obtained the accuracies of 95.83%, 92.9%, and 98.63% on three subsets of the dataset AS1, AS2, and AS3, respectively, using the protocols of [19].
Year
DOI
Venue
2020
10.1109/COMSNETS48256.2020.9027469
2020 INTERNATIONAL CONFERENCE ON COMMUNICATION SYSTEMS & NETWORKS (COMSNETS)
Keywords
DocType
ISSN
3D-skelton model, Deep learning, Human Action Recognition
Conference
2155-2487
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
M. Rashmi100.34
Ram Mohana Reddy Guddeti2488.76