A Skeleton-based View-Invariant Framework for Human Fall Detection in an Elevator

Rashid Ali*, Ivan Surya Hutomo, Lan Da Van, Yu Chee Tseng

*Corresponding author for this work

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

Abstract

This paper considers the emergency behavior detection problem inside an elevator. As elevators come in different shapes and emergency behavior data are scarce, we propose a skeleton-based view-invariant framework to tackle the camera view angle variation issue and the data collection issue. The proposed emergency fall detection model only needs to be trained for a target camera, which is deployed in an elevator at a manufacture's lab, from which a large amount of training data can be collected. The deployment of a source camera, which is in a customer-side elevator, hence can be customized and almost no training effort is needed. Our framework works in four stages. First, a 2D RGB input image is taken from the source camera and a 2D human skeleton is obtained by 2D pose estimation (AlphaPose). Second, the 2D skeleton is converted to a 3D human skeleton by 3D pose estimation (3D pose baseline). Third, a pre-trained rotation-translation (RT) transform (Procrustes analysis (PA)) aligns the 3D pose representations to the target camera view. Finally, a dual 3D pose baseline deep neural networks (D3PBDNN) model for human fall detection is proposed to perform the recognition task. We gather a human fall detection dataset inside different elevators from various view angles and validate our proposal. Experimental results successfully attain almost equivalent accuracy to that of a source camera-trained model.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Industrial Technology, ICIT 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728119489
DOIs
StatePublished - 2022
Event2022 IEEE International Conference on Industrial Technology, ICIT 2022 - Shanghai, China
Duration: 22 Aug 202225 Aug 2022

Publication series

NameProceedings of the IEEE International Conference on Industrial Technology
Volume2022-August

Conference

Conference2022 IEEE International Conference on Industrial Technology, ICIT 2022
Country/TerritoryChina
CityShanghai
Period22/08/2225/08/22

Keywords

  • 2D/3D pose estimation
  • deep neural network
  • fall detection
  • Procrustes analysis
  • skeleton
  • view-invariant

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