Fall Detection from Human Shape and Motion History Using Video Surveillance

  • Authors:
  • Caroline Rougier;Jean Meunier;Alain St-Arnaud;Jacqueline Rousseau

  • Affiliations:
  • Universite de Montreal, Canada;Universite de Montreal, Canada;Centre de sante et de services sociaux, Lucille-Teasdale, Canada;Universitaire de Geriatrie de Montreal, Canada

  • Venue:
  • AINAW '07 Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops - Volume 02
  • Year:
  • 2007

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Abstract

Nowadays, Western countries have to face the growing population of seniors. New technologies can help people stay at home by providing a secure environment and improving their quality of life. The use of computer vision systems offers a new promising solution to analyze people behavior and detect some unusual events. In this paper, we propose a new method to detect falls, which are one of the greatest risk for seniors living alone. Our approach is based on a combination of motion history and human shape variation. Our algorithm provides promising results on video sequences of daily activities and simulated falls.