Human Position Detection Using Point Cloud Data for Human-Robot Safety Systems

Nehal Amer, Jacqueline Humphries, Nitin Nandeshwar

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

Abstract

Artificial Intelligence algorithms have become extremely fast at identifying humans in real time. However, as Industry moves more towards collaborative working with robots and machines, the identification of humans is not sufficient. Knowledge of the exact location, and trajectory of the human movement is needed so that safe collaborative environments are built. This study presents an architecture that can be used to identify humans and extract their position in the space. The proposed system uses the Mask R-CNN instance segmentation model for identification of humans. The pixels which belong to each human in the image captured are identified. Then, the extracted pixels are mapped to the depth and point cloud images obtained from Azure Kinect camera to extract the positional data for each human in space. The impact of this research paper is that we propose a solution that captures XYZ co-ordinates of humans in a space.

Original languageEnglish
Title of host publication2022 7th International Conference on Mechanical Engineering and Robotics Research, ICMERR 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages219-223
Number of pages5
ISBN (Electronic)9781665490511
DOIs
Publication statusPublished - 2022
Event7th International Conference on Mechanical Engineering and Robotics Research, ICMERR 2022 - Virtual, Online, Poland
Duration: 9 Dec 202211 Dec 2022

Publication series

Name2022 7th International Conference on Mechanical Engineering and Robotics Research, ICMERR 2022

Conference

Conference7th International Conference on Mechanical Engineering and Robotics Research, ICMERR 2022
Country/TerritoryPoland
CityVirtual, Online
Period9/12/2211/12/22

Keywords

  • Point cloud
  • artificial intelligence
  • human detection
  • human-robot safety system

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