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Innovations in Crowd Management: An Integration of Visual Closure, Anthropometry, and Computer Vision

  • J. W. Chin
  • T. W. Wong
  • R. H. Y. So
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 822)

Abstract

This paper reports the application of a bio-inspired computational artificial intelligent (A.I.) real-time crowd monitoring and management system that integrates ergonomics, anthropometric database, computer vision and decision analytics. The system matches and fits anthropometrically customized 3D human models into a 3D space that is dynamically constructed from videos captured by one or more surveillance cameras. This approach is consistent with the human visual closure effect when we estimate the number of people in moving crowds. Dynamic human movement data are optimally extracted from the video data and used to construct and train a crowd movement profile detector. Learning algorithms have been developed to detect deviations from the normal profile. Results of validations show that there remains a huge gap in the performance between a bio-inspired computational A.I. model and a normal human-being in the surveillance tasks in terms of reliability, but this is a notable first step of a reliable crowd management system not emphasizing on facial feature extraction.

Keywords

Visual closure Anthropometry Computer vision 

Notes

Acknowledgement

The authors would like to thank the Shenzhen Science and Technology Innovation Committee (深圳市科技创新委员会) for partially supporting the work via Project JCYJ20170413173515472 (SZ-SZSTI1731). The study is also partially supported by the Innovation and Technology Commission via ITF Project ITS/170/15FP.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.HKUST-Shenzhen Research InstituteShenzhenChina
  2. 2.Department of Industrial Engineering and Decision AnalyticsHong Kong University of Science and TechnologyHong KongChina

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