A fast foreground object detection algorithm using Kernel Density Estimation

International Conference on Signal Processing Proceedings, ICSP

Volumn 1, Issue , 2012, Pages 703-707

  Li, Dawei ^a   Xu, Lihong ^a   Goodman, Erik ^b  

^a TONGJI UNIVERSITY   (China)

^b Michigan State University   (United States)

Author keywords

Foreground detection; Kernel density estimation; Markov random field; Noise reduction; Simulated annealing

Indexed keywords

BAYESIAN FRAMEWORKS; CLASSICAL METHODS; FOREGROUND DETECTION; FOREGROUND OBJECT DETECTION; KERNEL DENSITY ESTIMATION; MARKOV RANDOM FIELDS; MOVING OBJECT DETECTION ALGORITHMS; UNDESIRABLE EFFECTS;

MARKOV PROCESSES; NOISE ABATEMENT; OBJECT RECOGNITION; SIGNAL DETECTION; SIMULATED ANNEALING; STATISTICS;

ITERATIVE METHODS;

EID: 84876498997     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICoSP.2012.6491583     Document Type: Conference Paper

References (15)

1. C. Anderson, P. Burt, and G. Van der Wal, "Change Detection and Tracking Using Pyramid Transformation Techniques," in Proc. SPIEIntelligent Robots and Computer Vision. pp. 72-79, 1985.
2. R. J. Radke, S. Andra, O. Al-Kofahi, and B. Roysam, "Image Change Detection Algorithms: A Systematic Survey," IEEE Trans. Image Processing, vol. 14, no. 3, pp. 294-307, 2005.
3. C. Stauffer, and W. E. L. Grimson, "Learning Patterns of Activity Using Real-time Tracking," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 747-757, 2000.
4. C. Stauffer, and W. E. L. Grimson, "Adaptive Background Mixture Models for Real-time Tracking," in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Vol. 2, pp. 246-252, 1999.
5. K. Kim, T. H. Chalidabhongse, D. Harwood, and L. Davis, "Real-time Foreground-background Segmentation Using Codebook Model," Real-Time Imaging, vol. 11, pp. 172-185, 2005.
6. D. Comaniciu, and P. Meer, "Mean Shift: A Robust Approach Toward Feature Space Analysis," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 603-619, 2002.
7. D. W. Scott, Mulivariate Density Estimation. New York: Wiley-Interscience, 1992.
8. Y. Xu, L. Xu, D. Li, and Y. Wu, "Pedestrian Detection Using Background Subtraction Assisted Support Vector Machine," in: Proc. IEEE conf. on Intelligent Systems Design and Applications, 2011, pp. 837-842.
9. Q. Zhu, S. Avidan, M. Yeh, and K. Cheng, "Fast Human Detection Using a Cascade of Histogram of Oriented Gradients," in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. pp. 1491-1498, 2006.
10. T. Gevers, and A. Smeulders, "PicToSeek: Combining Color and Shape Invariant Features for Image Retrieval," IEEE Transactions on Image Processing, vol. 9, no. 1, pp. 102-119, 2000.
11. A. Elgammal, R. Duraiswami, D. Harwood, and L. Davis, "Background and Foreground Modeling Using Nonparametric Kernel Density Estimation for Visual Surveillance,", Proc. of the IEEE, vol. 90, pp. 1151-1163, 2002.
12. T. Aach, A. Kaup, and R. Mester, "Statistical Model-based Change Detection In Moving Video," Signal Processing, vol. 31, pp. 165-180, 1993.
13. J. M. McHugh, J. Konrad, V. Saligrama, and P. Jodoin, "Foreground-Adaptive Background Subtraction," Signal Processing Letters, vol. 16, no. 5, pp. 390-393, 2009.
14. S. Geman, and D. Geman, "Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 6, no. 6, pp. 721-741, 1984.
15. F. Wei, J. Jia, and Z. Liu, "Self-Validated Labeling of Markov Random Fields for Image Segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 10, pp. 1871-1887, 2010.