Poseidon Technologies,
1995.
HTML Version.
Vendor, Surveillance. Swimmer monitoring. The system saves lives.
A part of MG International which has non-vision swimming pool technologies.
Lu, W.[Wenmiao],
Tan, Y.P.[Yap-Peng],
A Vision-Based Approach to Early Detection of Drowning Incidents in
Swimming Pools,
CirSysVideo(14), No. 2, February 2004, pp. 159-178.
IEEE Abstract. IEEE Top Reference.
0403
BibRef
Earlier:
A camera-based system for early detection of drowning incidents,
ICIP02(III: 445-448).
IEEE Abstract. IEEE Top Reference.
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Kam, A.H.,
Lu, W.,
Yau, W.Y.,
A Video-Based Drowning Detection System,
ECCV02(IV: 297 ff.).
HTML Version.
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Cohen-Solal, E.[Eric],
Method and apparatus to distinguish deposit and removal in
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US_Patent6,731,805, May 4, 2004
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Brodsky, T.[Tomas],
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Video based detection of fall-down and other events,
US_Patent7,110,569, Sep 19, 2006
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McKenna, S.J.[Stephen J.],
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Summarising Contextual Activity and Detecting Unusual Inactivity
in a Supportive Home Environment,
PAA(7), No. 4, December 2004, pp. 386-401.
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BibRef
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Earlier: A2, A1:
Activity summarisation and fall detection in a supportive home
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ICPR04(IV: 323-326).
IEEE DOI Link
0409
BibRef
Tao, J.[Ji],
Tan, Y.P.[Yap-Peng],
A probabilistic approach to incorporating domain knowledge for
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SP:IC(19), No. 10, December 2004, pp. 959-974.
WWW Version.
0501
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Earlier:
People monitoring using face recognition with observation constraints,
ICIP04(III: 1437-1440).
IEEE DOI Link
0505
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Aubert, D.[Didier],
Guichard, F.[Frédéric],
Bouchafa, S.[Samia],
Time-scale change detection applied to real-time abnormal stationarity
monitoring,
RealTimeImg(10), No. 1, February 2004, pp. 9-22.
WWW Version.
0405
Develop a change detection algorithm. Use change detection over time to
find non-changes (i.e. people). Used in drowning detection system.
BibRef
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Guicard, F.,
Lavest, J.M.,
Liege, B.,
Meniere, J.,
Poseidon Technologies: The world's first and only computer-aided
drowning detection system,
CVPR01(Demos 17-18).
0110
See also Poseidon Technologies.
BibRef
Eng, H.L.[How-Lung],
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Robust Human Detection Within a Highly Dynamic Aquatic Environment in
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IP(15), No. 6, June 2006, pp. 1583-1600.
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Earlier:
Novel region-based modeling for human detection within highly dynamic
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0502
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ICPR04(II: 257-260).
IEEE DOI Link
0409
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Eng, H.L.[How-Lung],
Toh, K.A.[Kar-Ann],
Wah, A.H.K.S.[Alvin Harvey Kam Siew],
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Yau, W.Y.[Wei-Yun],
An automatic drowning detection surveillance system for challenging
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IEEE DOI Link
0311
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Eng, H.L.[How-Lung],
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Yau, W.Y.[Wei-Yun],
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ISVC05(371-379).
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0512
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Eng, H.L.,
Toh, K.A.,
Yau, W.Y.,
Wang, J.,
DEWS: A Live Visual Surveillance System for Early Drowning Detection at
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IEEE DOI Link
0803
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Lu, S.J.[Si-Jun],
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PR(40), No. 8, August 2007, pp. 2173-2184.
WWW Version.
0704
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A Knowledge-Based Approach for Detecting Unattended Packages in
Surveillance Video,
AVSBS06(110-110).
IEEE DOI Link
0611
Unattended package; Human activity recognition; Eigen-features; SVM; HMM
BibRef
Lu, S.J.[Si-Jun],
Zhang, J.[Jian],
Feng, D.D.[David Dagan],
An efficient method for detecting ghost and left objects in
surveillance video,
AVSBS07(540-545).
IEEE DOI Link
0709
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Anderson, D.[Derek],
Luke, R.H.[Robert H.],
Keller, J.M.[James M.],
Skubic, M.[Marjorie],
Rantz, M.[Marilyn],
Aud, M.[Myra],
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and fuzzy logic,
CVIU(113), No. 1, January 2009, pp. 80-89.
Elsevier DOI Link
WWW Version.
0812
Linguistic summarization; Activity analysis; Fuzzy logic; Fall
detection; Eldercare; Voxel person
BibRef
Yang, J.H.[Jeong-Hwa],
Schilit, B.N.[Bill N.],
McDonald, D.W.[David W.],
Activity Recognition for the Digital Home,
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IEEE DOI Link
0804
BibRef
Thome, N.[Nicolas],
Miguet, S.[Serge],
Ambellouis, S.,
A Real-Time, Multiview Fall Detection System: A LHMM-Based Approach,
CirSysVideo(18), No. 11, November 2008, pp. 1522-1532.
IEEE DOI Link
0811
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Earlier: A1, A2, Only:
A HHMM-Based Approach for Robust Fall Detection,
ICARCV06(1-8).
IEEE DOI Link
0612
BibRef
Earlier: A1, A2, Only:
A robust appearance model for tracking human motions,
AVSBS05(528-533).
IEEE DOI Link
0602
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Thome, N.[Nicolas],
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Learning articulated appearance models for tracking humans:
A spectral graph matching approach,
SP:IC(23), No. 10, November 2008, pp. 769-787,.
Elsevier DOI Link
WWW Version.
0804
BibRef
Earlier:
Human Body Part Labeling and Tracking Using Graph Matching Theory,
AVSBS06(38-38).
IEEE DOI Link
0611
Real-time multiple people tracking; On-line articulated appearance
learning; People identification; Body part labeling from silhouette;
Spectral graph matching; Topological model
BibRef
Pop, I.[Ionel],
Scuturici, M.[Mihaela],
Miguet, S.[Serge],
Incremental trajectory aggregation in video sequences,
ICPR08(1-4).
IEEE DOI Link
0812
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Kerhet, A.[Aliaksei],
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0712
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Magno, M.[Michele],
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Brunelli, D.[Davide],
Di Stefano, L.[Luigi],
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Multimodal Abandoned/Removed Object Detection for Low Power Video
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AVSBS09(188-193).
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Zouba, N.[Nadia],
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Multisensor Fusion for Monitoring Elderly Activities at Home,
AVSBS09(98-103).
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Monitoring Activities of Daily Living (ADLs) of Elderly Based on 3D Key
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CogVis08(37-50).
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0805
Human Pose. Model video events from small set of postures.
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Foroughi, H.[Homa],
Rezvanian, A.[Alireza],
Paziraee, A.[Amirhossien],
Robust Fall Detection Using Human Shape and Multi-class Support Vector
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0812
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San Miguel, J.C.[Juan Carlos],
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Robust Unattended and Stolen Object Detection by Fusing Simple
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AVSBS08(18-25).
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0809
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Atev, S.[Stefan],
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0809
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Sofka, M.[Michal],
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Liao, H.H.[Huei-Hung],
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A Localized Approach to Abandoned Luggage Detection with
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AVSBS08(132-139).
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0809
See also Commentary Paper 2 on A Localized Approach to Abandoned Luggage Detection with Foreground-Mask Sampling.
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Nicolescu, M.[Mircea],
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0809
See also Localized Approach to Abandoned Luggage Detection with Foreground-Mask Sampling, A.
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Prati, A.[Andrea],
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AVSBS08(142-142).
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0809
See also Localized Approach to Abandoned Luggage Detection with Foreground-Mask Sampling, A.
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Atienza-Vanacloig, V.,
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Hazelhoff, L.[Lykele],
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Bambang-Oetomo, S.[Sidarto],
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Behavioral State Detection of Newborns Based on Facial Expression
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0909
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Hazelhoff, L.[Lykele],
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Video-Based Fall Detection in the Home Using Principal Component
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0810
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Distante, C.[Cosimo],
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A multi-sensor approach for People Fall Detection in home environment,
M2SFA208(xx-yy).
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Procrustes Shape Analysis for Fall Detection,
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0810
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Vishwakarma, V.[Vinay],
Mandal, C.[Chittaranjan],
Sural, S.[Shamik],
Automatic Detection of Human Fall in Video,
PReMI07(616-623).
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0712
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Ferrando, S.,
Gera, G.[Gianluca],
Regazzoni, C.S.[Carlo S.],
Classification of Unattended and Stolen Objects in Video-Surveillance
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AVSBS06(21-21).
IEEE DOI Link
0611
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Savogin, I.,
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Fasoli, C.,
Portable monitoring and localization systems for disabled patients,
IEVM06(xx-yy).
PDF Version.
0609
BibRef
Dedeoglu, Y.[Yigithan],
Töreyin, B.U.[B. Ugur],
Güdükbay, U.[Ugur],
Çetin, A.E.[A. Enis],
Silhouette-Based Method for Object Classification and Human Action
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CVHCI06(64-77).
Springer DOI Link
0605
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Töreyin, B.U.[B. Ugur],
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HMM Based Falling Person Detection Using Both Audio and Video,
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0601
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Action Recognition in a Wearable Assistance System,
ICPR06(II: 1254-1258).
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0609
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Beynon, M.D.,
van Hook, D.J.,
Seibert, M.,
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Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Intruders, Intrusions, Intrusion Detection .