@inproceedings{a81c9b1d58944fabad7a7a12647122b4,
title = "Improving Accessibility of Remote Drone Control with a Streamlined Computer Vision Approach",
abstract = "The purpose of this work is to develop a method for classifying hand signals and using the pre-diction output in a drone control algorithm. To achieve this, methods based on Convolutional Neural Networks (CNNs) were applied. The hand signals chosen were the numerical hand signs for one through five for two-dimensional movement with a separate idle signal, and a fist for land. A script was created to automate one minute of training image capture for each class. Transfer learning with PyTorch (Python) was performed using a pre-Trained 18-layer residual learning network (ResNet-18). The training process completed in three minutes and 43 seconds with five epochs and a final overall validation accuracy of over 99\%. Implemented with the drone control, the classification performed as desired at approximately 60 predictions per second on desktop and 20 predictions per second on a Nvidia Jetson Nano.",
keywords = "Convolutional Neural Network, Drones, Human-Machine Interaction",
author = "Evan Lowhorn and Rocio Alba-Flores",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International IOT, Electronics and Mechatronics Conference, IEMTRONICS 2022 ; Conference date: 01-06-2022 Through 04-06-2022",
year = "2022",
doi = "10.1109/IEMTRONICS55184.2022.9795813",
language = "English",
series = "2022 IEEE International IOT, Electronics and Mechatronics Conference, IEMTRONICS 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Satyajit Chakrabarti and Rajashree Paul and Bob Gill and Malay Gangopadhyay and Sanghamitra Poddar",
booktitle = "2022 IEEE International IOT, Electronics and Mechatronics Conference, IEMTRONICS 2022",
address = "United States",
}