Action Recognition of Construction Machinery from Simulated Training Data Using Video Filters

Jinhyeok Sim, Jun Younes Louhi Kasahara, Shota Chikushi, Hiroshi Yamakawa, Yusuke Tamura, Keiji Nagatani, Takumi Chiba, Shingo Yamamoto, Kazuhiro Chayama, Atsushi Yamashita, Hajime Asama

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

Abstract

In the construction industry, continuous monitoring of actions performed by construction machinery is a critical task in order to achieve improved productivity and efficiency. However, measuring and recording each individual construction machinery's actions is both time consuming and expensive if conducted manually by humans. Therefore, automatic action recognition of construction machinery is highly desirable. Inspired by the success of Deep Learning approaches for human action recognition, there has been an increased number of studies dealing with action recognition of construction machinery using Deep Learning. However, those approaches require large amounts of training data, which is difficult to obtain since construction machinery are usually located in the field. Therefore, this paper proposes a method for action recognition of construction machinery using only training data generated from a simulator, which is much easier to obtain than actual training data. In order to bridge the feature domain gap between simulator-generated data and actual field data, a video filter was used. Experiments using a model of an excavator, one of the most commonly used construction machinery, showed the potential of our proposed method.

Original languageEnglish
Title of host publicationProceedings of the 37th International Symposium on Automation and Robotics in Construction, ISARC 2020
Subtitle of host publicationFrom Demonstration to Practical Use - To New Stage of Construction Robot
PublisherInternational Association on Automation and Robotics in Construction (IAARC)
Pages595-599
Number of pages5
ISBN (Electronic)9789529436347
Publication statusPublished - 2020
Event37th International Symposium on Automation and Robotics in Construction: From Demonstration to Practical Use - To New Stage of Construction Robot, ISARC 2020 - Kitakyushu, Online, Japan
Duration: 2020 Oct 272020 Oct 28

Publication series

NameProceedings of the 37th International Symposium on Automation and Robotics in Construction, ISARC 2020: From Demonstration to Practical Use - To New Stage of Construction Robot

Conference

Conference37th International Symposium on Automation and Robotics in Construction: From Demonstration to Practical Use - To New Stage of Construction Robot, ISARC 2020
Country/TerritoryJapan
CityKitakyushu, Online
Period20/10/2720/10/28

Keywords

  • Action recognition
  • Deep learning
  • Video filter

ASJC Scopus subject areas

  • Artificial Intelligence
  • Civil and Structural Engineering
  • Human-Computer Interaction
  • Geotechnical Engineering and Engineering Geology

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