Abhirami J.S,
Dharunya V,
Subhadharuna R,
Tirukkala Gnana Sindhuja,
- Assistant Professor, Nehru Institute of Engineering and Technology, Anna University, Coimbatore, India
- UG Student, Nehru Institute of Engineering and Technology, Anna University, Coimbatore, India
- UG Student, Nehru Institute of Engineering and Technology, Anna University, Coimbatore, India
- UG Student, Nehru Institute of Engineering and Technology, Anna University, Coimbatore, India
Abstract
This project proposes a novel approach for gesture recognition using key point extraction and neural networks. Our proposed system leverages key point extraction techniques to capture fine-grained spatial information from input gestures. These key points are then fed into a neural network model, allowing for automatic feature learning and robust gesture classification. The goal of this project is to integrate OpenCV’s computer vision capabilities to build a flexible and effective home automation system. The system uses cameras to process images, which enables it to identify and react to different environmental cues in the house. Using a mobile or web application, a user-friendly interface makes it easier to remotely monitor and operate household appliances. A clever and flexible way to improve security, energy efficiency, and general convenience in a home is this home automation system, which combines the capabilities of OpenCV for image processing with Arduino for real-time control. The modular design of this project ensures scalability and relevance in the quickly developing field of smart home automation by allowing future extensions and integration with upcoming technologies.
Keywords: Gesture recognition, Virtual reality control system, robust gesture recognition, Sign language recognition, CNN
References
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Journal of Microcontroller Engineering and Applications
| Volume | |
| Received | May 7, 2024 |
| Accepted | May 18, 2024 |
| Published | August 14, 2024 |