Gesture Recognition and Showing a Cyclist's Intent

Thomas Bridgeman, Helena Gibson, Kevin Meehan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publication2023 IEEE World AI IoT Congress, AIIoT 2023
EditorsSatyajit Chakrabarti, Rajashree Paul
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages656-661
Number of pages6
ISBN (Electronic)9798350337617
DOIs
Publication statusPublished - 2023
Event2023 IEEE World AI IoT Congress, AIIoT 2023 - Virtual, Online, United States
Duration: 7 Jun 202310 Jun 2023

Publication series

Name2023 IEEE World AI IoT Congress, AIIoT 2023

Conference

Conference2023 IEEE World AI IoT Congress, AIIoT 2023
Country/TerritoryUnited States
CityVirtual, Online
Period7/06/2310/06/23

Keywords

  • Bicycle
  • CNN
  • Gesture Recognition
  • MediaPipe

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