Preventing Drowning Accidents Using Thermal Cameras

Søren Bonderup, Jonas Lundgaard Olsson, Morten Bojesen Bonderup, Thomas B. Moeslund

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

Resumé

Every year approximately 372 000 people die from unintentional drowning, causing it to be a top-3 cause to unintentional injury [1]. In Denmark 25% of drownings happen at harbor areas [2]. To address this problem thermal cameras have been placed strategically at a harbor. Using computer vision techniques an automatic surveillance system for predicting and detecting drowning accidents has been implemented. First a person detector has been implemented using simple human characteristics. The person is tracked using a Kalman Filter. Using the tracker as a prior, a fall prediction is determined. A fall detector is implemented using a virtual trip-wire in combination with an optical flow algorithm making the system able to detect 100% of all falls and only yielding a 0.08 false positive rate hourly. The entire system has been developed using 155 h of real life thermal video, hereof 56 h are manually annotated.
OriginalsprogEngelsk
TitelAdvances in Visual Computing
RedaktørerGeorge Bebis, Richard Boyle, Bahram Parvin, Darco Koracin, Faith Porikili, Sandra Skaff, Alireza Entezari, Jianjuan Min, Daisuke Iwai, Amela Sadagic, Carlos Scheidegger, Tobias Isenberg
Antal sider12
ForlagSpringer
Publikationsdato2016
Sider111-122
ISBN (Trykt)978-3-319-50831-3
ISBN (Elektronisk)978-3-319-50832-0
DOI
StatusUdgivet - 2016
BegivenhedISVC16: 12th International Symposium on Visual Computing - Monte Carlo Resort & Casino, Las Vegas, USA
Varighed: 12 dec. 201614 dec. 2016
http://www.isvc.net

Konference

KonferenceISVC16: 12th International Symposium on Visual Computing
LokationMonte Carlo Resort & Casino
LandUSA
ByLas Vegas
Periode12/12/201614/12/2016
Internetadresse
NavnLecture Notes in Computer Science
Vol/bind10073
ISSN0302-9743

Fingerprint

Ports and harbors
Accidents
Cameras
Detectors
Heat problems
Optical flows
Kalman filters
Computer vision
Wire
Hot Temperature

Citer dette

Bonderup, S., Olsson, J. L., Bonderup, M. B., & Moeslund, T. B. (2016). Preventing Drowning Accidents Using Thermal Cameras. I G. Bebis, R. Boyle, B. Parvin, D. Koracin, F. Porikili, S. Skaff, A. Entezari, J. Min, D. Iwai, A. Sadagic, C. Scheidegger, ... T. Isenberg (red.), Advances in Visual Computing (s. 111-122). Springer. Lecture Notes in Computer Science, Bind. 10073 https://doi.org/10.1007/978-3-319-50832-0_12
Bonderup, Søren ; Olsson, Jonas Lundgaard ; Bonderup, Morten Bojesen ; Moeslund, Thomas B. / Preventing Drowning Accidents Using Thermal Cameras. Advances in Visual Computing. red. / George Bebis ; Richard Boyle ; Bahram Parvin ; Darco Koracin ; Faith Porikili ; Sandra Skaff ; Alireza Entezari ; Jianjuan Min ; Daisuke Iwai ; Amela Sadagic ; Carlos Scheidegger ; Tobias Isenberg. Springer, 2016. s. 111-122 (Lecture Notes in Computer Science, Bind 10073).
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title = "Preventing Drowning Accidents Using Thermal Cameras",
abstract = "Every year approximately 372 000 people die from unintentional drowning, causing it to be a top-3 cause to unintentional injury [1]. In Denmark 25{\%} of drownings happen at harbor areas [2]. To address this problem thermal cameras have been placed strategically at a harbor. Using computer vision techniques an automatic surveillance system for predicting and detecting drowning accidents has been implemented. First a person detector has been implemented using simple human characteristics. The person is tracked using a Kalman Filter. Using the tracker as a prior, a fall prediction is determined. A fall detector is implemented using a virtual trip-wire in combination with an optical flow algorithm making the system able to detect 100{\%} of all falls and only yielding a 0.08 false positive rate hourly. The entire system has been developed using 155 h of real life thermal video, hereof 56 h are manually annotated.",
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Bonderup, S, Olsson, JL, Bonderup, MB & Moeslund, TB 2016, Preventing Drowning Accidents Using Thermal Cameras. i G Bebis, R Boyle, B Parvin, D Koracin, F Porikili, S Skaff, A Entezari, J Min, D Iwai, A Sadagic, C Scheidegger & T Isenberg (red), Advances in Visual Computing. Springer, Lecture Notes in Computer Science, bind 10073, s. 111-122, ISVC16: 12th International Symposium on Visual Computing, Las Vegas, USA, 12/12/2016. https://doi.org/10.1007/978-3-319-50832-0_12

Preventing Drowning Accidents Using Thermal Cameras. / Bonderup, Søren; Olsson, Jonas Lundgaard; Bonderup, Morten Bojesen; Moeslund, Thomas B.

Advances in Visual Computing. red. / George Bebis; Richard Boyle; Bahram Parvin; Darco Koracin; Faith Porikili; Sandra Skaff; Alireza Entezari; Jianjuan Min; Daisuke Iwai; Amela Sadagic; Carlos Scheidegger; Tobias Isenberg. Springer, 2016. s. 111-122 (Lecture Notes in Computer Science, Bind 10073).

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

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N2 - Every year approximately 372 000 people die from unintentional drowning, causing it to be a top-3 cause to unintentional injury [1]. In Denmark 25% of drownings happen at harbor areas [2]. To address this problem thermal cameras have been placed strategically at a harbor. Using computer vision techniques an automatic surveillance system for predicting and detecting drowning accidents has been implemented. First a person detector has been implemented using simple human characteristics. The person is tracked using a Kalman Filter. Using the tracker as a prior, a fall prediction is determined. A fall detector is implemented using a virtual trip-wire in combination with an optical flow algorithm making the system able to detect 100% of all falls and only yielding a 0.08 false positive rate hourly. The entire system has been developed using 155 h of real life thermal video, hereof 56 h are manually annotated.

AB - Every year approximately 372 000 people die from unintentional drowning, causing it to be a top-3 cause to unintentional injury [1]. In Denmark 25% of drownings happen at harbor areas [2]. To address this problem thermal cameras have been placed strategically at a harbor. Using computer vision techniques an automatic surveillance system for predicting and detecting drowning accidents has been implemented. First a person detector has been implemented using simple human characteristics. The person is tracked using a Kalman Filter. Using the tracker as a prior, a fall prediction is determined. A fall detector is implemented using a virtual trip-wire in combination with an optical flow algorithm making the system able to detect 100% of all falls and only yielding a 0.08 false positive rate hourly. The entire system has been developed using 155 h of real life thermal video, hereof 56 h are manually annotated.

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A2 - Skaff, Sandra

A2 - Entezari, Alireza

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A2 - Iwai, Daisuke

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A2 - Scheidegger, Carlos

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PB - Springer

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Bonderup S, Olsson JL, Bonderup MB, Moeslund TB. Preventing Drowning Accidents Using Thermal Cameras. I Bebis G, Boyle R, Parvin B, Koracin D, Porikili F, Skaff S, Entezari A, Min J, Iwai D, Sadagic A, Scheidegger C, Isenberg T, red., Advances in Visual Computing. Springer. 2016. s. 111-122. (Lecture Notes in Computer Science, Bind 10073). https://doi.org/10.1007/978-3-319-50832-0_12