Background subtraction for static & moving camera

Proceedings - International Conference on Image Processing, ICIP

Volumn 2015-December, Issue , 2015, Pages 4530-4534

  Sajid, Hasan ^a   Cheung, Sen Ching Samson ^a  

^a UNIVERSITY OF KENTUCKY   (United States)

Author keywords

background model bank; background modelling; Background Subtraction; binary classifiers

Indexed keywords

EID: 84956605637     PISSN: 15224880     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIP.2015.7351664     Document Type: Conference Paper

References (28)

1. H. Sajid and S.-C. S. Cheung, "Background subtraction under sudden illumination change, " in Multimedia Signal Processing (MMSP), 2014 IEEE 16th International Workshop on. IEEE, 2014, pp. 1-6.
2. Y. Wang, P.-M. Jodoin, F. Porikli, J. Konrad, Y. Benezeth, and P. Ishwar, "Cdnet 2014: An expanded change detection benchmark dataset, " in Computer Vision and Pattern Recognition Workshops (CVPRW), 2014 IEEE Conference on. IEEE, 2014, pp. 393-400.
3. T. Gevers, A. Gijsenij, J. Van de Weijer, and J.-M. Geusebroek, Color in computer vision: Fundamentals and applications. John Wiley & Sons, 2012, vol. 23.
4. T. Horprasert, D. Harwood, and L. S. Davis, "A statistical approach for real-time robust background subtraction and shadow detection, " in IEEE ICCV, vol. 99, 1999, pp. 1-19.
5. Z. Chen, N. Pears, M. Freeman, and J. Austin, "Background subtraction in video using recursive mixture models, spatiotemporal filtering and shadow removal, " in Advances in Visual Computing. Springer, 2009, pp. 1141-1150.
6. S. C. Sen-Ching and C. Kamath, "Robust techniques for background subtraction in urban traffic video, " in Electronic Imaging 2004. International Society for Optics and Photonics, 2004, pp. 881-892.
7. S. Brutzer, B. Hoferlin, and G. Heidemann, "Evaluation of background subtraction techniques for video surveillance, " in Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on. IEEE, 2011, pp. 1937-1944.
8. C. Stauffer and W. E. L. Grimson, "Adaptive background mixture models for real-time tracking, " in Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on., vol. 2. IEEE, 1999.
9. A. Elgammal, D. Harwood, and L. Davis, "Non-parametric model for background subtraction, " in Computer Vision-ECCV 2000. Springer, 2000, pp. 751-767.
10. R. Wang, F. Bunyak, G. Seetharaman, and K. Palaniappan, "Static and moving object detection using flux tensor with split Gaussian models, " in Computer Vision and Pattern Recognition Workshops (CVPRW), 2014 IEEE Conference on. IEEE, 2014, pp. 420-424.
11. P. D. Z. Varcheie, M. Sills-Lavoie, and G.-A. Bilodeau, "A multiscale region-based motion detection and background subtraction algorithm, " Sensors, vol. 10, no. 2, pp. 1041-1061, 2010.
12. Z. Zivkovic and F. van der Heijden, "Efficient adaptive density estimation per image pixel for the task of background subtraction, " Pattern recognition letters, vol. 27, no. 7, pp. 773-780, 2006.
13. Z. Zivkovic, "Improved adaptive Gaussian mixture model for background subtraction, " in Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, vol. 2. IEEE, 2004, pp. 28-31.
14. O. Barnich and M. Van Droogenbroeck, "Vibe: A universal background subtraction algorithm for video sequences, " Image Processing, IEEE Transactions on, vol. 20, no. 6, pp. 1709-1724, 2011.
15. M. Seki, T. Wada, H. Fujiwara, and K. Sumi, "Background subtraction based on cooccurrence of image variations, " in Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on, vol. 2. IEEE, 2003, pp. II-65.
16. P. W. Power and J. A. Schoonees, "Understanding background mixture models for foreground segmentation, " in Proceedings image and vision computing New Zealand, vol. 2002, 2002, pp. 10-11.
17. J. Pilet, C. Strecha, and P. Fua, "Making background subtraction robust to sudden illumination changes, " in Computer Vision-ECCV 2008. Springer, 2008, pp. 567-580.
18. N. M. Oliver, B. Rosario, and A. P. Pentland, "A Bayesian computer vision system for modeling human interactions, " Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 22, no. 8, pp. 831-843, 2000.
19. L. P. Vosters, C. Shan, and T. Gritti, "Background subtraction under sudden illumination changes. " in AVSS, 2010, pp. 384-391.
20. P.-L. St-Charles, G.-A. Bilodeau, and R. Bergevin, "Subsense: A universal change detection method with local adaptive sensitivity, " Image Processing, IEEE Transactions on, vol. 24, no. 1, pp. 359-373, 2014.
21. M. D. Gregorio and M. Giordano, "Change detection with weightless neural networks, " in Computer Vision and Pattern Recognition Workshops (CVPRW), 2014 IEEE Conference on. IEEE, 2014, pp. 409-413.
22. M. Sedky, M. Moniri, and C. C. Chibelushi, "Spectral-360: A physics-based technique for change detection, " in Computer Vision and Pattern Recognition Workshops (CVPRW), 2014 IEEE Conference on. IEEE, 2014, pp. 405-408.
23. B. Wang and P. Dudek, "A fast self-tuning background subtraction algorithm, " in Computer Vision and Pattern Recognition Workshops (CVPRW), 2014 IEEE Conference on. IEEE, 2014, pp. 401-404.
24. L. Maddalena and A. Petrosino, "The sobs algorithm: what are the limits?" in Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on. IEEE, 2012, pp. 21-26.
25. S. Varadarajan, P. Miller, and H. Zhou, "Spatial mixture of Gaussians for dynamic background modelling, " in Advanced Video and Signal Based Surveillance (AVSS), 2013 10th IEEE International Conference on. IEEE, 2013, pp. 63-68.
26. L. Maddalena and A. Petrosino, "A fuzzy spatial coherencebased approach to background/foreground separation for moving object detection, " Neural Computing and Applications, vol. 19, no. 2, pp. 179-186, 2010.
27. Y. Benezeth, P.-M. Jodoin, B. Emile, H. Laurent, and C. Rosenberger, "Comparative study of background subtraction algorithms, " Journal of Electronic Imaging, vol. 19, no. 3, pp. 033003-033003, 2010.
28. X. Lu, "A multiscale spatio-temporal background model for motion detection, " in Image Processing (ICIP), 2014 IEEE International Conference on. IEEE, 2014, pp. 3268-3271.