Deep background subtraction with scene-specific convolutional neural networks

International Conference on Systems, Signals, and Image Processing

Volumn 2016-June, Issue , 2016, Pages

  Braham, Marc ^a   Van Droogenbroeck, Marc ^a  

^a UNIVERSITY OF LIÈGE   (Belgium)

Author keywords

Background subtraction; CDnet; change detection; convolutional neural networks; deep learning; surveillance

Indexed keywords

CONVOLUTION; IMAGE SEGMENTATION; NEURAL NETWORKS; SPACE SURVEILLANCE;

BACKGROUND SUBTRACTION; CDNET; CHANGE DETECTION; CONVOLUTIONAL NEURAL NETWORK; DEEP LEARNING;

IMAGE PROCESSING;

EID: 84978831998     PISSN: 21578672     EISSN: 21578702     Source Type: Conference Proceeding    
DOI: 10.1109/IWSSIP.2016.7502717     Document Type: Conference Paper

References (22)

1. C. Stauffer and E. Grimson, "Adaptive background mixture models for real-time tracking, " in IEEE Int. Conf. Comput. Vision and Pattern Recogn. (CVPR), vol. 2, pp. 246-252, June 1999.
2. A. Elgammal, D. Harwood, and L. Davis, "Non-parametric model for background subtraction, " in European Conf. Comput. Vision (ECCV), vol. 1843 of Lecture Notes Comp. Sci., pp. 751-767, Springer, June 2000.
3. P.-L. St-Charles, G.-A. Bilodeau, and R. Bergevin, "A self-adjusting approach to change detection based on background word consensus, " in IEEE Winter Conf. Applicat. Comp. Vision (WACV), pp. 990-997, Jan. 2015.
4. T. Bouwmans, "Traditional and recent approaches in background modeling for foreground detection: An overview, " Computer Science Review, vol. 11-12, pp. 31-66, May 2014.
5. P.-M. Jodoin, S. Piérard, Y. Wang, and M. Van Droogenbroeck, "Overview and benchmarking of motion detection methods, " in Background Modeling and Foreground Detection for Video Surveillance, ch. 24, Chapman and Hall/CRC, July 2014.
6. A. Schick, M. Bauml, and R. Stiefelhagen, "Improving foreground segmentation with probabilistic superpixel Markov Random Fields, " in IEEE Int. Conf. Comput. Vision and Pattern Recog. Workshop (CVPRW), pp. 27-31, June 2012.
7. P.-L. St-Charles, G.-A. Bilodeau, and R. Bergevin, "SuBSENSE: A universal change detection method with local adaptive sensitivity, " IEEE Trans. Image Process., vol. 24, pp. 359-373, Jan. 2015.
8. O. Barnich and M. Van Droogenbroeck, "ViBe: A universal background subtraction algorithm for video sequences, " IEEE Trans. Image Process., vol. 20, pp. 1709-1724, June 2011.
9. S. Gruenwedel, P. Van Hese, and W. Philips, "An edge-based approach for robust foreground detection, " in Advanced Concepts for Intelligent Vision Syst. (ACIVS), vol. 6915 of Lecture Notes Comp. Sci., pp. 554- 565, Springer, Aug. 2011.
10. M. Heikkilä and M. Pietikäinen, "A texture-based method for modeling the background and detecting moving objects, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, pp. 657-662, Apr. 2006.
11. S. Zhang, H. Yao, S. Liu, X. Chen, and W. Gao, "A covariance-based method for dynamic background subtraction, " in IEEE Int. Conf. Pattern Recogn. (ICPR), pp. 1-4, Dec. 2008.
12. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition, " Proc. of IEEE, vol. 86, pp. 2278- 2324, Nov. 1998.
13. N. Goyette, P.-M. Jodoin, F. Porikli, J. Konrad, and P. Ishwar, "A novel video dataset for change detection benchmarking, " IEEE Trans. Image Process., vol. 23, pp. 4663-4679, Nov. 2014.
14. P. Sermanet, S. Chintala, and Y. LeCun, "Convolutional neural networks applied to house numbers digit classification, " in IEEE Int. Conf. Pattern Recogn. (ICPR), pp. 3288-3291, Nov. 2012.
15. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, "Going deeper with convolutions, " in IEEE Int. Conf. Comput. Vision and Pattern Recogn. (CVPR), pp. 1-9, June 2015.
16. C. Farabet, C. Couprie, L. Najman, and Y. LeCun, "Learning hierarchical features for scene labeling, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, pp. 1915-1929, Aug. 2013.
17. B. Laugraud, S. Piérard, M. Braham, and M. Van Droogenbroeck, "Simple median-based method for stationary background generation using background subtraction algorithms, " in Int. Conf. Image Anal. and Process. (ICIAP), Workshop Scene Background Modeling and Initialization (SBMI), vol. 9281 of Lecture Notes Comp. Sci., pp. 477- 484, Springer, Sept. 2015.
18. S. Bianco, G. Ciocca, and R. Schettini, "How far can you get by combining change detection algorithms, " CoRR, vol. abs/1505. 02921, 2015.
19. F. D. G. Allebosch, P. Veelart, and W. Philips, "EFIC: Edge based foreground background segmentation and interior classification for dynamic camera viewpoints, " in Advanced Concepts for Intelligent Vision Syst. (ACIVS), vol. 9386 of Lecture Notes Comp. Sci., pp. 130-141, Springer, Oct. 2015.
20. M. Sedky, M. Moniri, and C. Chibelushi, "Spectral 360: A physics-based technique for change detection, " in IEEE Int. Conf. Comput. Vision and Pattern Recog. Workshop (CVPRW), pp. 399-402, June 2014.
21. L. Maddalena and A. Petrosino, "The SOBS algorithm: what are the limits, " in IEEE Int. Conf. Comput. Vision and Pattern Recog. Workshop (CVPRW), pp. 21-26, June 2012.
22. A. Miron and A. Badii, "Change detection based on graph cuts, " in IEEE Int. Conf. Syst., Signals and Image Process. (IWSSIP), pp. 273- 276, Sept. 2015.