A Fuzzy Background Modeling Approach for Motion Detection in Dynamic Backgrounds

Communications in Computer and Information Science

Volumn 346, Issue , 2012, Pages 177-185

  Zhao, Zhenjie ^a   Bouwmans, Thierry ^b   Zhang, Xuebo ^a   Fang, Yongchun ^a  

^a NANKAI UNIVERSITY   (China)

^b UNIVERSITÉ DE LA ROCHELLE   (France)

Author keywords

dynamic backgrounds; motion detection; MRF; T2 FGMM

Indexed keywords

BAYESIAN FRAMEWORKS; DYNAMIC BACKGROUND; EVALUATION RESULTS; GAUSSIAN MIXTURE MODEL; MARKOV RANDOM FIELDS; MOTION DETECTION; MRF; T2-FGMM;

IMAGE SEGMENTATION; MARKOV PROCESSES; SIGNAL PROCESSING;

MODELS;

EID: 84882245475     PISSN: 18650929     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-35286-7_23     Document Type: Conference Paper

References (19)

1. Radke, R.J., Andra, S., Al-Kofahi, O., Roysam, B.: Image change detection algorithms: a systematic survey. IEEE Transactions on Image Processing 14(3), 294-307 (2005)
2. Piccardi, M.: Background subtraction techniques: a review. In: IEEE International Conference on Systems, Man and Cybernetics, vol. 4, pp. 3099-3104 (2004)
3. Brutzer, S., Hoferlin, B., Heideman, G.: Evaluation of background subtraction techniques for video surveillance. In: CVPR 2011, pp. 1937-1944 (2011)
4. Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: IEEE CVPR 1999 (1999)
5. Zhou, Y., Xu, W., Tao, H., Gong, Y.: Background segmentation using spatial-temporal multi-resolution MRF. In: IEEE Workshops on Application of Computer Vision, vol. 1, pp. 8-13 (2005)
6. Monnet, A., Mittal, A., Paragios, N., Ramesh, V.: Background modeling and subtraction of dynamic scenes. In: CVPR 2003, vol. 2, pp. 1305-1312 (2003)
7. Mahadevan, V., Vansconcelos, N.: Background subtraction in highly dynamic scenes. In: CVPR 2008, pp. 1-6 (2008)
8. El Baf, F., Bouwmans, T., Vachon, B.: Type-2 Fuzzy Mixture of Gaussians Model: Application to Background Modeling. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Remagnino, P., Porikli, F., Peters, J., Klosowski, J., Arns, L., Chun, Y.K., Rhyne, T.-M., Monroe, L. (eds.) ISVC 2008, Part I. LNCS, vol. 5358, pp. 772-781. Springer, Heidelberg (2008)
9. El Baf, F., Bouwmans, T., Vachon, B.: Fuzzy Statistical Modeling of Dynamic Backgrounds for Moving Object Detection in Infrared Videos. In: IEEE CVPR Workshops, pp. 60-65 (2009)
10. Zeng, J., Xie, L., Liu, Z.: Type-2 fuzzy Gaussian mixture models. Pattern Recognition 41(12), 3636-3643 (2008)
11. Wren, C., Azarbayejani, A., Darrell, T., Pentland, A.: Pfinder: Real-time tracking of the human body. PAMI 19(7), 780-785 (1997)
12. Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.S.: Real-time foregroundbackground segmentation using codebook Model. Real-Time Image 11, 172-185 (2005)
13. Elgammal, A., Harwood, D., Davis, L.: Non-parametric Model for Background Subtraction. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 751-767. Springer, Heidelberg (2000)
14. Barnich, O., Van Droogenbroeck, M.: ViBe: A Universal Background Subtraction Algorithm for Video Sequences. Image Processing 20(6), 1709-1724 (2011)
15. Maddalena, L., Petrosino, A.: The SOBS Algorithm: What Are the Limits? In: IEEE Workshop on Change Detection, CVPR 2012 (2012)
16. Bouwmans, T.: Background Subtraction for Visual Surveillance: A Fuzzy Approach. In: Bouwmans, T. (ed.) Handbook on Soft Computing for Video Surveillance, ch. 5. Taylor and Francis Group (2012)
17. Li, S.: Markov Random Field Models in Computer Vision. In: Eklundh, J.-O. (ed.) ECCV 1994. LNCS, vol. 801, pp. 361-370. Springer, Heidelberg (1994)
18. Zivkovic, Z.: Improved Adaptive Gaussian Mixture Model for Background Subtraction. In: International Conference on Pattern Recognition, vol. 2, pp. 28-31 (2004)
19. IEEE CVPR 2012 Workshops on Change Detection (2012), http://www.changedetection.net