Action Recognition for Controlling Electronic Appliances
Rajalakshmi J1, N.Duraimurugan2, S.P.Chokkalingam3
1Rajalakshmi J, M.E. Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai (Tamil Nadu), India.
2N.Duraimurugan, Assistant Professor, Department of Computer Science Engineering, Rajalakshmi Engineering College, Thandalam Chennai (Tamil Nadu), India.
3S.P.Chokkalingam, Professor, Department of CSE, Saveetha School of Engineering, Chennai (Tamil Nadu), India.
Manuscript received on 29 May 2019 | Revised Manuscript received on 11 June 2019 | Manuscript Published on 22 June 2019 | PP: 565-567 | Volume-8 Issue-3S, February 2019 | Retrieval Number: C11210283S19/19©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The objective is to develop a system for controlling electronic appliances by recognizing human action. Generally, sensors are used for tracing the pattern of action recognition. Human action recognition and feature extraction are the main challenges of the system, which can be effectively overcome by using deep learning techniques. This approach uses deep learning technique by combining both RNN and CNN network for action recognition either in the form of images or signals. Combining CNN and RNN will enhance the ability to recognize different actions at varied time span.
Keywords: CNN-Convolution Neural Network, RNN-Recurrent Neural Network.
Scope of the Article: Pattern Recognition