Flexible background subtraction with self-balanced local sensitivity

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

Volumn , Issue , 2014, Pages 414-419

  St Charles, Pierre Luc ^a   Bilodeau, Guillaume Alexandre ^a   Bergevin, Robert ^b  

^a ÉCOLE POLYTECHNIQUE DE MONTRÉAL   (Canada)

^b UNIVERSITÉ LAVAL   (Canada)

Author keywords

background subtraction; change detection; feedback; local binary similarity patterns; subsense; Video surveillance

Indexed keywords

FEEDBACK; PIXELS; SECURITY SYSTEMS;

BACKGROUND SUBTRACTION; CHANGE DETECTION; SIMILARITY PATTERNS; SUBSENSE; VIDEO SURVEILLANCE;

COMPUTER VISION;

EID: 84908519124     PISSN: 21607508     EISSN: 21607516     Source Type: Conference Proceeding    
DOI: 10.1109/CVPRW.2014.67     Document Type: Conference Paper

References (21)

1. O. Barnich and M. Van Droogenbroeck. Vibe: A universal background subtraction algorithm for video sequences. Image Processing, 2011 IEEE Transactions on, 20(6):1709-1724, 2011. 2, 3, 5
2. Y. Benezeth, P.-M. Jodoin, B. Emile, H. Laurent, and C. Rosenberger. Comparative study of background subtraction algorithms. Journal of Electronic Imaging, 19, 07 2010. 5
3. G.-A. Bilodeau, J.-P. Jodoin, and N. Saunier. Change detection in feature space using local binary similarity patterns. In Computer and Robot Vision (CRV), 2013 International Conference on, pages 106-112, 2013. 1, 2
4. M. V. Droogenbroeck and O. Paquot. Background subtraction: Experiments and improvements for vibe. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on, pages 32-37. IEEE, 2012. 2, 3, 5
5. A. M. Elgammal, D. Harwood, and L. S. Davis. Nonparametric model for background subtraction. In Proceedings of the 6th European Conference on Computer Vision (ECCV), pages 751-767, 2000. 5
6. N. Goyette, P.-M. Jodoin, F. Porikli, J. Konrad, and P. Ishwar. Changedetection. net: A new change detection benchmark dataset. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on, pages 1-8, 2012. 1, 2, 4, 5
7. M. Heikkila and M. Pietikainen. A texture-based method for modeling the background and detecting moving objects. Pattern Analysis and Machine Intelligence, 2006 IEEE Transactions on, 28(4):657-662, 2006. 2
8. M. Hofmann, P. Tiefenbacher, and G. Rigoll. Background segmentation with feedback: The pixel-based adaptive segmenter. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on, pages 38-43, 2012. 2, 3, 5
9. J.-P. Jodoin, G.-A. Bilodeau, and N. Saunier. Background subtraction based on local shape. CoRR, abs/1204. 6326, 2012. 5
10. L. Maddalena and A. Petrosino. A self-organizing approach to background subtraction for visual surveillance applications. IEEE Transactions on Image Processing, 17(7):1168-1177, July 2008. 5
11. 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, pages 21-26, 2012. 5
12. A. Morde, X. Ma, and S. Guler. Learning a background model for change detection. In Computer Vision and Pattern RecognitionWorkshops (CVPRW), 2012 IEEE Computer Society Conference on, pages 15-20, 2012. 5
13. Y. Nonaka, A. Shimada, H. Nagahara, and R. Taniguchi. Evaluation report of integrated background modeling based on spatio-temporal features. In Computer Vision and Pattern RecognitionWorkshops (CVPRW), 2012 IEEE Computer Society Conference on, pages 9-14, 2012. 5
14. F. Porikli and O. Tuzel. Bayesian background modeling for foreground detection. In Proceedings of the third ACM international workshop on Video surveillance & sensor networks, VSSN '05, pages 55-58, New York, NY, USA, 2005. ACM. 5
15. A. Schick, M. Bauml, and R. Stiefelhagen. Improving foreground segmentations with probabilistic superpixel markov random fields. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on, pages 27-31, 2012. 5
16. P.-L. St-Charles and G.-A. Bilodeau. Improving background subtraction using local binary similarity patterns. In Applications of Computer Vision (WACV), 2014 IEEE Computer Society Winter Conference on, 2014. 1
17. C. Stauffer andW. E. L. Grimson. Adaptive background mixture models for real-time tracking. In Computer Vision and Pattern Recognition (CVPR), 1999 IEEE Computer Society Conference on, volume 2, pages-252 Vol. 2, 1999. 2, 5
18. P. D. Z. Varcheie, M. Sills-Lavoie, and G.-A. Bilodeau. A multiscale region-based motion detection and background subtraction algorithm. Sensors, 10(2):1041-1061, 2010. 5
19. S. Yoshinaga, A. Shimada, H. Nagahara, and R. Taniguchi. Background model based on intensity change similarity among pixels. In Frontiers of Computer Vision, (FCV), 2013 19th Korea-Japan Joint Workshop on, pages 276-280, 2013. 5
20. Z. Zivkovic. Improved adaptive gaussian mixture model for background subtraction. In Pattern Recognition (ICPR), 2004 International Conference on, volume 2, pages 28-31 Vol. 2, 2004. 5
21. Z. Zivkovic and F. van der Heijden. Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recognition Letters, 27(7):773-780, 2006. 5