Background subtraction in highly dynamic scenes

26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR

Volumn , Issue , 2008, Pages

  Mahadevan, Vijay ^a   Vasconcelos, Nuno ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER VISION; FEATURE EXTRACTION; IMAGE PROCESSING; IMAGE SEGMENTATION; IMAGING TECHNIQUES; PATTERN RECOGNITION; PHOTOGRAPHY; VIDEO RECORDING;

BACKGROUND SUBTRACTION; BACKGROUND SUBTRACTION ALGORITHM; BINARY CLASSIFICATION; BIOLOGICAL VISIONS; CAMERA MOTIONS; DUAL PROBLEMS; DYNAMIC SCENES; DYNAMIC TEXTURES; LOCAL FEATURES; NEW ALGORITHM; NO TRAINING; SALIENCY DETECTION; VIDEO SEQUENCING;

LEARNING ALGORITHMS;

EID: 51949094565     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2008.4587576     Document Type: Conference Paper

References (25)

1. http://staff.science.uva.nl/zivkovic/download.html.
2. A. Bugeau and P. Perez. Detection and segmentation of moving objects in highly dynamic scenes. In CVPR, 2007.
3. M. Carandini, J. Demb, V. Mante, D. Tolhurst, Y. Dan, B. Olshausen, J. Gallant, and N. Rust. Do we know what the early visual system does? J. Neurosci., 25, 2005.
4. A. B. Chan and N. Vasconcelos. Efficient computation of the kl divergence between dynamic textures. Technical Report SVCL-TR-2004-02, Dept. of ECE, UCSD, 2004.
5. A. B. Chan and N. Vasconcelos. Probabilistic kernels for the classification of auto-regressive visual processes. In CVPR, volume 1, pages 846-851, 2005.
6. T. Cover and J. Thomas. Elements of Information Theory. John Wiley & Sons Inc., New York, 1991.
7. G. Doretto, A. Chiuso, Y. N. Wu, and S. Soatto. Dynamic textures. IJCV, 51(2):91-109, 2003.
8. A. Elgammal, D. Harwood, and L. Davis. Non-parametric model for background subtraction. In ECCV, pages 751-757, 2000.
9. D. Gao and N. Vasconcelos. Discriminant saliency for visual recognition from cluttered scenes. In Proc. NIPS, Vancouver, Canada, 2005.
10. D. Gao and N. Vasconcelos. Bottom-up saliency is a discriminant process. In ICCV, 2007.
11. D. Gao and N. Vasconcelos. V1 is an optimal saliency detector. In Computational Cognitive Neuroscience Conference (CCNC), 2007.
12. J. Huang and D. Mumford. Statistics of Natural Images and Models. In CVPR, pages 541-547, 1999.
13. L. Itti. The ilab neuromorphic vision c++ toolkit: Free tools for the next generation of vision algorithms. The Neuromorphic Engineer, 1(1):10, Mar 2004.
14. L. Itti and P. Baldi. A principled approach to detecting surprising events in video. In CVPR, pages 631-637, 2005.
15. Y. Li. On incremental and robust subspace learning. Pattern Recognition, 37(7):1509-19, 2004.
16. A. Monnet, A. Mittal, N. Paragios, and V. Ramesh. Background modeling and subtraction of dynamic scenes. In CVPR, 2003.
17. A. Murray, D. Basu. Motion tracking with an active camera. IEEE Trans. PAMI, 16(5):449-459, 1994.
18. P. V. Overschee and B. D. Moor. N4sid: Subspace algorithms for the identification of combined deterministic-stochastic systems. Automatica, 30:75-93, 1994.
19. Y. Ren, C. Chua, and Y. Ho. Motion detection with nonstationary background. Machine Vision and Applications, 13(5-6):332-343, 2003.
20. Y. Sheikh and M. Shah. Bayesian modeling of dynamic scenes for object detection. IEEE PAMI, 27(11):1778-92.
21. R. Shumway and D. Stoffer. An approach to time series smoothing andforecasting using the EM algorithm. Journal of Time Series Analysis, 3(4):433-467, 1982.
22. C. Stauffer and W. Grimson. Adaptive background mixture models for real-time tracking. In Proc. CVPR, 1999.
23. N. Vasconcelos. Feature selection by maximum marginal diversity. In Proc. NIPS, Vancouver, Canada, 2002.
24. L. Wixson. Detecting salient motion by accumulating directionally- consistent flow. IEEE PAMI, 22(8):774-780, 2000.
25. Z. Zivkovic. Improved adaptive Gaussian mixture model for background subtraction. In ICPR, 2004.