DEWS

A live visual surveillance system for early drowning detection at pool

How Lung Eng, Kar Ann Toh, Wei Yun Yau, Junxian Wang

Research output: Contribution to journalArticle

36 Citations (Scopus)

Abstract

A real-time vision system operating at an outdoor swimming pool is presented in this paper. The system is designed to automatically recognize different swimming activities and to detect occurrence of early drowning incidents. We have named this system the Drowning Early Warning System (DEWS). One key challenge we faced in the problem is the relatively high level of noise in the steps of foreground detection and behavior recognition. Therefore, a set of methods in the fields of background subtraction, denoising, data fusion and blob splitting are proposed, which have been motivated by characteristics of aquatic background and crowded scenario at the pool. In the step to detect an early drowning incident, visual indicators of distress and drowning are incorporated through a set of foreground descriptors. A module comprising data fusion and hidden Markov modeling is developed to learn unique traits of different swimming behaviors, in particular, those early drowning events. The experiment of this work reports realistic on-site evaluations performed. Examples of interesting behaviors, i.e., distress, drowning, treading and numerous swimming styles, are simulated and collected. Experimental results show that we have established a prototype system which is robust and beyond the stage of proof-of-concept.

Original languageEnglish
Article number4399966
Pages (from-to)196-210
Number of pages15
JournalIEEE Transactions on Circuits and Systems for Video Technology
Volume18
Issue number2
DOIs
Publication statusPublished - 2008 Feb 1

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Alarm systems
Data fusion
Swimming pools
Swimming
Experiments

All Science Journal Classification (ASJC) codes

  • Media Technology
  • Electrical and Electronic Engineering

Cite this

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abstract = "A real-time vision system operating at an outdoor swimming pool is presented in this paper. The system is designed to automatically recognize different swimming activities and to detect occurrence of early drowning incidents. We have named this system the Drowning Early Warning System (DEWS). One key challenge we faced in the problem is the relatively high level of noise in the steps of foreground detection and behavior recognition. Therefore, a set of methods in the fields of background subtraction, denoising, data fusion and blob splitting are proposed, which have been motivated by characteristics of aquatic background and crowded scenario at the pool. In the step to detect an early drowning incident, visual indicators of distress and drowning are incorporated through a set of foreground descriptors. A module comprising data fusion and hidden Markov modeling is developed to learn unique traits of different swimming behaviors, in particular, those early drowning events. The experiment of this work reports realistic on-site evaluations performed. Examples of interesting behaviors, i.e., distress, drowning, treading and numerous swimming styles, are simulated and collected. Experimental results show that we have established a prototype system which is robust and beyond the stage of proof-of-concept.",
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DEWS : A live visual surveillance system for early drowning detection at pool. / Eng, How Lung; Toh, Kar Ann; Yau, Wei Yun; Wang, Junxian.

In: IEEE Transactions on Circuits and Systems for Video Technology, Vol. 18, No. 2, 4399966, 01.02.2008, p. 196-210.

Research output: Contribution to journalArticle

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