@inproceedings{4281b24b9f8d4c37a209c05a721037cf,
title = "Gesture Recognition and Showing a Cyclist's Intent",
abstract = "Gesture recognition is an important step forward for both humans and machines but especially the interaction between them. The transport industry is a sector that can benefit greatly due to the reliance on using one's hands to steer a car or cycle a bike. Bicycles often prove to be challenging regarding showing the intent of the cyclist to drivers, other cyclists and pedestrians. This paper showcases the development of a gesture recognition system that translates a cyclist's gestures to better show their intentions to other road users through the use of an Arduino and LED. The solution uses the MediaPipe framework and a Convolutional Neural Network. It was trained and tested on using a custom dataset. There were four different classes of gestures (neutral, stop, direction and thanks) and provides a high degree of accuracy in recognition.",
keywords = "Bicycle, CNN, Gesture Recognition, MediaPipe",
author = "Thomas Bridgeman and Helena Gibson and Kevin Meehan",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE World AI IoT Congress, AIIoT 2023 ; Conference date: 07-06-2023 Through 10-06-2023",
year = "2023",
doi = "10.1109/AIIoT58121.2023.10174468",
language = "English",
series = "2023 IEEE World AI IoT Congress, AIIoT 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "656--661",
editor = "Satyajit Chakrabarti and Rajashree Paul",
booktitle = "2023 IEEE World AI IoT Congress, AIIoT 2023",
}