Faculty Mentor
Dr. Rocio Alba-Flores
Location
Poster 110
Session Format
Poster Presentation
Academic Unit
Department of Electrical and Computer Engineering
Keywords
Allen E. Paulson College of Engineering and Computing Student Research Symposium, Unmanned Aerial Systems, UAVs, Convolutional Neural Networks, CNN
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Presentation Type and Release Option
Presentation (Open Access)
Start Date
2022 12:00 AM
January 2022
Real-time Video Streams of Hand Gestures to Control Unmanned Aerial Systems (UAVs)
Poster 110
Over past few years, unmanned aircraft vehicles (UAVs) have been becoming more and more popular for various purposes such as surveillance, automated industry, robotics, vehicle guidance, traffic monitoring and control system. It is very important to have multiple methods of UAVs controlling to fit in UAVs usages. The goal of this work is to develop a technique to control an UAV using 8 different hand gestures. To achieve that, a hand key point detection algorithm is developed to detect 21 key points of the hand. Then, those key points are used as the input to an intelligent system based on Convolutional Neural Networks (CNN) that is able to classify the hand gestures. After archiving hand gesture classifications, unique command for UAVs control is assigned to each hand gesture; and a programming is built in Python to send those commands to an UAV for operation.
