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Human-AGV Interaction: Real-Time Gesture Detection Using Deep Learning

  • Jiliang Zhang
  • , Li Peng*
  • , Wei Feng
  • , Zhaojie Ju
  • , Honghai Liu
  • *Corresponding author for this work
  • Jiangnan University
  • University of Portsmouth

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

Abstract

In this paper, we present a real-time human body gesture recognition for controlling Automated Guided Vehicle (AGV) in facility. Exploiting the breakthrough of deep convolutional networks in computers, we have developed a system that can detect the human gestures and give corresponding commands to the AGV according to different gestures. For avoiding interference of multiple operational targets in an image, we proposed a method to filter out the non-operator. In addition, we propose a human gesture interpreter with clear semantic information and build a new human gesture dataset with 8 gestures to train or fine-tune the deep neural networks for human gesture detection. In order to balance accuracy and response speed, we choose MobileNet-SSD as the detection network.

Original languageEnglish
Title of host publicationIntelligent Robotics and Applications - 12th International Conference, ICIRA 2019, Proceedings
EditorsHaibin Yu, Jinguo Liu, Lianqing Liu, Yuwang Liu, Zhaojie Ju, Dalin Zhou
PublisherSpringer Verlag
Pages231-242
Number of pages12
ISBN (Print)9783030275402
DOIs
StatePublished - 2019
Externally publishedYes
Event12th International Conference on Intelligent Robotics and Applications, ICIRA 2019 - Shenyang, China
Duration: 8 Aug 201911 Aug 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11744 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th International Conference on Intelligent Robotics and Applications, ICIRA 2019
Country/TerritoryChina
CityShenyang
Period8/08/1911/08/19

Keywords

  • AGV
  • Deep learning
  • Human gesture
  • MobileNet-SSD

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