A Gesture Recognition System for Cranes Using Deep Learning with a Self-attention Mechanism

Keigo Watanabe, Maierdan Maimaitimin, Kazuki Yamamoto, Isaku Nagai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This research is aimed at recognizing the gesture of a lifting coordinator and automating the operation of a crane by introducing a system with deep learning. This paper first explains the outline of a gesture recognition system, and describes skeletal detection and its accuracy improvement technique. Furthermore, a gesture recognition system is constructed using a 1DCNN, and the recognition accuracy is verified to be improved by introducing a self-attention mechanism.

Original languageEnglish
Title of host publicationProceedings of 2022 SICE International Symposium on Control Systems, SICE ISCS 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages87-94
Number of pages8
ISBN (Electronic)9784907764746
DOIs
Publication statusPublished - 2022
Event2022 SICE International Symposium on Control Systems, SICE ISCS 2022 - Virtual, Online, Japan
Duration: Mar 8 2022Mar 10 2022

Publication series

NameProceedings of 2022 SICE International Symposium on Control Systems, SICE ISCS 2022

Conference

Conference2022 SICE International Symposium on Control Systems, SICE ISCS 2022
Country/TerritoryJapan
CityVirtual, Online
Period3/8/223/10/22

Keywords

  • Crane
  • Deep Learning
  • Gesture Recognition
  • Skeleton Detection

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Optimization
  • Computer Vision and Pattern Recognition

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