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Volumn , Issue , 2009, Pages 2066-2073

Realtime background subtraction from dynamic scenes

Author keywords

[No Author keywords available]

Indexed keywords

BACKGROUND SCENES; BACKGROUND SUBTRACTION; CENTRAL COMPONENT; CLOSED FORM; DYNAMIC SCENES; EMPIRICAL STUDIES; FRAMES PER SECONDS; GRAPHICS PROCESSOR; LARGE MARGIN PRINCIPLE; MISTAKE BOUNDS; MOVING-OBJECT DETECTION; NONSTATIONARY; OFFLINE; ONE-CLASS SUPPORT VECTOR MACHINE; ONLINE LEARNING; REAL TIME; REAL TIME ANALYSIS; SPATIAL INTERACTION; SPATIO-TEMPORAL CHANGES; TEMPORAL CHANGE;

EID: 77953221990     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2009.5459454     Document Type: Conference Paper
Times cited : (24)

References (28)
  • 1
    • 4544304381 scopus 로고    scopus 로고
    • On the generalization ability of on-line learning algorithms
    • N. Cesa-bianchi, A. Conconi, and C. Gentile. On the generalization ability of on-line learning algorithms. IEEE TIT, 50:2050-2057, 2004.
    • (2004) IEEE TIT , vol.50 , pp. 2050-2057
    • Cesa-bianchi, N.1    Conconi, A.2    Gentile, C.3
  • 3
    • 50849105799 scopus 로고    scopus 로고
    • Background subtraction for temporally irregular dynamic textures
    • G. Dalle, J. Migdal, and W. Grimson. Background subtraction for temporally irregular dynamic textures. In WACV, 2008.
    • (2008) WACV
    • Dalle, G.1    Migdal, J.2    Grimson, W.3
  • 4
    • 0001267733 scopus 로고    scopus 로고
    • Non-parametric model for background subtraction
    • A. Elgammal, D. Harwood, and L. Davis. Non-parametric model for background subtraction. In ECCV, 2000.
    • (2000) ECCV
    • Elgammal, A.1    Harwood, D.2    Davis, L.3
  • 5
    • 33947253926 scopus 로고    scopus 로고
    • Real-time stereo matching using orthogonal reliability-based dynamic programming
    • DOI 10.1109/TIP.2006.891344
    • M. Gong and Y.-H. Yang. Real-time stereo matching using orthogonal reliability-based dynamic programming. IEEE TIP, 16(3):879-884, 2007. (Pubitemid 46431980)
    • (2007) IEEE Transactions on Image Processing , vol.16 , Issue.3 , pp. 879-884
    • Gong, M.1    Yang, Y.-H.2
  • 7
    • 51949108825 scopus 로고    scopus 로고
    • Real time object tracking based on dynamic feature grouping with background subtraction
    • Z. Kim. Real time object tracking based on dynamic feature grouping with background subtraction. In CVPR, 2008.
    • (2008) CVPR
    • Kim, Z.1
  • 8
  • 9
    • 0008815681 scopus 로고    scopus 로고
    • Exponentiated gradient versus gradient descent for linear predictors
    • January
    • J. Kivinen and M. K. Warmuth. Exponentiated gradient versus gradient descent for linear predictors. Information and Computation, 132(1):1-64, January 1997.
    • (1997) Information and Computation , vol.132 , Issue.1 , pp. 1-64
    • Kivinen, J.1    Warmuth, M.K.2
  • 10
    • 34249656812 scopus 로고    scopus 로고
    • Minimizing nonsubmodular functions with graph cuts - A review
    • V. Kolmogorov and C. Rother. Minimizing nonsubmodular functions with graph cuts-a review. IEEE T. PAMI, 29:1274-1279, 2007.
    • (2007) IEEE T. PAMI , vol.29 , pp. 1274-1279
    • Kolmogorov, V.1    Rother, C.2
  • 12
    • 34248560655 scopus 로고    scopus 로고
    • Discriminative learning can succeed where generative learning fails
    • P. Long, R. Servedio, and H. Simon. Discriminative learning can succeed where generative learning fails. Inf. Process. Lett., 103(4):131-135, 2007.
    • (2007) Inf. Process. Lett. , vol.103 , Issue.4 , pp. 131-135
    • Long, P.1    Servedio, R.2    Simon, H.3
  • 13
    • 51949094565 scopus 로고    scopus 로고
    • Background subtraction in highly dynamic scenes
    • V. Mahadevan and N. Vasconcelos. Background subtraction in highly dynamic scenes. In CVPR, 2008.
    • (2008) CVPR
    • Mahadevan, V.1    Vasconcelos, N.2
  • 14
    • 35348830375 scopus 로고    scopus 로고
    • Background subtraction using markov thresholds
    • J. Migdal and W. Grimson. Background subtraction using markov thresholds. In WMVC, pages 58-65, 2005.
    • (2005) WMVC , pp. 58-65
    • Migdal, J.1    Grimson, W.2
  • 15
    • 0344983250 scopus 로고    scopus 로고
    • Background modeling and subtraction of dynamic scenes
    • A. Monnet, A. Mittal, N. Paragios, and V. Ramesh. Background modeling and subtraction of dynamic scenes. In ICCV, 2003.
    • (2003) ICCV
    • Monnet, A.1    Mittal, A.2    Paragios, N.3    Ramesh, V.4
  • 16
    • 56749153838 scopus 로고    scopus 로고
    • Making background subtraction robust to sudden illumination changes
    • J. Pilet, C. Strecha, and P. Fua. Making background subtraction robust to sudden illumination changes. In ECCV, pages 567-580, 2008.
    • (2008) ECCV , pp. 567-580
    • Pilet, J.1    Strecha, C.2    Fua, P.3
  • 19
    • 28044439637 scopus 로고    scopus 로고
    • Bayesian modeling of dynamic scenes for object detection
    • Y. Sheikh and M. Shah. Bayesian modeling of dynamic scenes for object detection. IEEE T. PAMI, 27(11):1778-1792, 2005.
    • (2005) IEEE T. PAMI , vol.27 , Issue.11 , pp. 1778-1792
    • Sheikh, Y.1    Shah, M.2
  • 20
    • 24644476577 scopus 로고    scopus 로고
    • Bayesian object detection in dynamic scenes
    • Y. Sheikh and M. Shah. Bayesian object detection in dynamic scenes. In CVPR, 2005.
    • (2005) CVPR
    • Sheikh, Y.1    Shah, M.2
  • 22
    • 0034244889 scopus 로고    scopus 로고
    • Learning patterns of activity using real-time tracking
    • C. Stauffer and W. Grimson. Learning patterns of activity using real-time tracking. IEEE T. PAMI, 22:747-757, 2000.
    • (2000) IEEE T. PAMI , vol.22 , pp. 747-757
    • Stauffer, C.1    Grimson, W.2
  • 24
    • 0033285765 scopus 로고    scopus 로고
    • Wallflower: Principles and practice of background maintenance
    • K. Toyama, J. Krumm, B. Brumitt, and B. Meyers. Wallflower: Principles and practice of background maintenance. In ICCV, 1999.
    • (1999) ICCV
    • Toyama, K.1    Krumm, J.2    Brumitt, B.3    Meyers, B.4
  • 25
    • 24944537843 scopus 로고    scopus 로고
    • Large margin methods for structured and interdependent output variables
    • I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun. Large margin methods for structured and interdependent output variables. J. Mach. Learn. Res., 6:1453-1484, 2005.
    • (2005) J. Mach. Learn. Res. , vol.6 , pp. 1453-1484
    • Tsochantaridis, I.1    Joachims, T.2    Hofmann, T.3    Altun, Y.4
  • 27
    • 0344551957 scopus 로고    scopus 로고
    • Segmenting foreground objects from a dynamic textured background via a robust Kalman filter
    • J. Zhong and S. Sclaroff. Segmenting foreground objects from a dynamic textured background via a robust Kalman filter. In ICCV, 2003.
    • (2003) ICCV
    • Zhong, J.1    Sclaroff, S.2
  • 28
    • 3042585857 scopus 로고    scopus 로고
    • Recursive unsupervised learning of finite mixture models
    • Z. Zivkovic and F. Heijden. Recursive unsupervised learning of finite mixture models. IEEE T. PAMI, 26(5), 2004.
    • (2004) IEEE T. PAMI , vol.26 , Issue.5
    • Zivkovic, Z.1    Heijden, F.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.