Skip to content

Human-AGV interaction: real-time gesture detection using deep learning

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

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
Subtitle of host publication12th International Conference, ICIRA 2019, Shenyang, China, August 8–11, 2019, Proceedings, Part V
EditorsHaibin Yu, Jinguo Liu, Lianquing Liu, Zhaojie Ju, Yuwang Liu, Dalin Zhou
Number of pages12
ISBN (Electronic)978-3-030-27541-9
ISBN (Print)978-3-030-27540-2
Publication statusPublished - 6 Aug 2019
Event12th International Conference on Intelligent Robotics and Applications - Shenyang, China
Duration: 8 Aug 201911 Aug 2019

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameLecture Notes in Artificial Intelligence
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference12th International Conference on Intelligent Robotics and Applications
Abbreviated titleICIRA 2019


  • Zhang_Human-AGV Interaction_ICIRA2019_002_final_v3-2

    Rights statement: This is a post-peer-review, pre-copyedit version of an article published in Intelligent Robotics and Applications. ICIRA 2019. The final authenticated version is available online at:

    Accepted author manuscript (Post-print), 526 KB, PDF document

Related information

Relations Get citation (various referencing formats)

ID: 15487569