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This study presents a hierarchical background modelling and subtraction approach for real-time detection of moving objects. At the first level, a novel pixel-wise background modelling method is proposed for coarse detection. The method can dynamically assign the optimal number of components for each pixel with the borrow–lend strategy. And a flexible learning rate which is variable and different for each component is presented to adapt to scene changes. Additionally, a new mechanism using a framework of finite state machine is introduced to maintain and update the background models. At the second level, in order to deal with sudden illumination changes, a block-wise foreground validation approach is adopted for refined detection. The authors compare the proposed approach with state-of-the-art methods and experimental results under various scenes demonstrate the robustness and effectiveness of the proposed approach.
References
-
-
1)
-
23. Poppe, C., Martens, G., Bruyne, S.D., Lambert, P., Walle, R.V.: ‘Robust spatio-temporal multimodal background subtraction for video surveillance’, Opt. Eng., 2008, 47, (10), pp. 107203 (doi: 10.1117/1.3002325).
-
2)
-
30. Bouwmans, T., El Baf, F., Vachon, B.: ‘Background modeling using mixture of Gaussians for foreground detection – a survey’, Recent Pat. Comput. Sci., 2008, 1, (3), pp. 219–237 (doi: 10.2174/2213275910801030219).
-
3)
-
34. Bertozzi, M., Broggi, A., Rose, M.D., Felisa, M., Rakotomamonjy, A., Suard, F.: ‘A pedestrian detector using histograms of oriented gradients and a support vector machine classifier’. Proc. IEEE Conf. Intelligent Transportation Systems, 2007, pp. 143–148.
-
4)
-
5)
-
16. Camplani, M., Salgado, L.: ‘Adaptive background modeling in multicamera system for real-time object’, Opt. Eng., 2011, 50, (12), p. 127206 (doi: 10.1117/1.3662422).
-
6)
-
37. Maddalena, L., Petrosino, A.: ‘The SOBS algorithm: what are the limits?’. Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition Workshops, 2012, pp. 21–26.
-
7)
-
30. Vosters, L., Shan, C., Gritti, T.: ‘Background subtraction under sudden illumination changes’. Proc. IEEE Int. Conf. Advanced Video and Signal Based Surveillance, 2010, pp. 384–391.
-
8)
-
14. Tan, R., Huo, H., Qian, J., Fang, T.: ‘Traffic video segmentation using adaptive-K Gaussian mixture model’. Int. Workshop on Intelligent Computing, 2006, pp. 125–134.
-
9)
-
12. Shimada, A., Arita, D., Taniguchi, R.: ‘Dynamic control of adaptive mixture of Gaussians background model’. Proc. IEEE Int. Conf. Advanced Video and Signal Based Surveillance, 2006, p. 5.
-
10)
-
27. Greiffenhagen, M., Ramesh, V., Niemann, H.: ‘The systematic design and analysis cycle of a vision system: a case study in video surveillance’. Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition, 2001, vol. 2, pp. 704–711.
-
11)
-
K. Kim ,
T. Chalidabhongse ,
D. Harwood ,
L. Davis
.
Real-time foreground–background segmentation using code-book model.
Real-Time Imaging
,
3 ,
172 -
185
-
12)
-
17. Elgammal, A., Duraiswami, R., Harwood, D., Davis, L.S.: ‘Background and foreground modeling using nonparametric kernel density estimation for video surveillance’. Proc. IEEE, 2002, vol. 90, no. (7), pp. 1151–1163.
-
13)
-
20. Pokrajac, D., Latecki, L.J.: ‘Spatiotemporal blocks-based moving objects identification and tracking’. Proc. IEEE Visual Surveillance and Performance Evaluation of Tracking and Surveillance, 2003, pp. 70–77.
-
14)
-
C. Stauffer ,
W. Grimson
.
Learning patterns of activity using real time tracking.
IEEE Trans. Pattern Anal. Mach. Intell.
,
8 ,
747 -
757
-
15)
-
24. Geng, L., Xiao, Z.T.: ‘Real time foreground-background segmentation using two-layer codebook model’. Proc. IEEE Int. Conf. Control, Automation and Systems Engineering, 2011, pp. 1–5.
-
16)
-
21. Zhao, Y.D., Gong, H.F., Jia, Y.D., Zhu, S.C.: ‘Background modeling by subspace learning on spatio-temporal patches’, Pattern Recognit. Lett., 2012, 33, pp. 1134–1147 (doi: 10.1016/j.patrec.2012.01.012).
-
17)
-
28. Ming, Y., Yang, B., Men, A., Guo, Y.: ‘Background subtraction under single varying illumination’. Proc. IEEE Int. Conf. Broadband Network & Multimedia Technology, 2009, pp. 143–146.
-
18)
-
38. Brutzer, S., Hoferlin, B., Heidemann, G.: ‘Evaluation of background subtraction techniques for video surveillance’. Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2011, pp. 1937–1944.
-
19)
-
9. Carminati, L., Benois-Pineau, J.: ‘Gaussian mixture classification for moving object detection in video surveillance environment’. Proc. IEEE Int. Conf. Image processing, 2005, vol. 3, pp. III–113–116.
-
20)
-
32. Klare, B., Sarkar, S.: ‘Background subtraction in varying illuminations using an ensemble based on an enlarged feature set’. Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition Workshops, 2009, pp. 66–73.
-
21)
-
33. Javed, O., Shafique, K., Shah, M.: ‘A hierarchical approach to robust background subtraction using color and gradient information’. Proc. IEEE Workshop on Motion and Video Computing, 2002, pp. 22–27.
-
22)
-
18. Fang, X.H., Xiong, W., Hu, B.J., Wang, L.T.: ‘A moving object detection algorithm based on color’. Proc. Int. Symp. Instrumentation Science and Technology, 2006, vol. 48, pp. 384–387.
-
23)
-
19. Barnich, O., Van Droogenbroeck, M.: ‘Vibe: a universal background subtraction algorithm for video sequences’, IEEE Trans. Image Process., 2011, 20, (6), pp. 1709–1724 (doi: 10.1109/TIP.2010.2101613).
-
24)
-
1. Piccardi, M.: ‘Background subtraction techniques: a review’. Proc. Int. Conf. Systems, Man and Cybernetics, Hague, Netherlands, 2004, pp. 3199–3204.
-
25)
-
29. Stefano, L.D., Tombari, F., Mattoccia, S., Lisi, E.D.: ‘Robust and accurate change detection under sudden illuminations variations’. Proc. ACCV Workshop on Multi-dimensional and Multi-view Image Processing, 2007, vol. 3.
-
26)
-
39. Goyette, N., Jodoin, P.M., Porikli, F., Konrad, J., Ishwar, P.: ‘Changedetection.net: a new change detection benchmark dataset’. Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition Workshops, 2012, pp. 1–8.
-
27)
-
35. Toyama, K., Krumm, J., Brumitt, B., Meyers, B.: ‘Wallflower: principles and practice of background maintenance’. Proc. IEEE Conf. Computer Vision, 1999, vol. 1, pp. 255–261.
-
28)
-
29)
-
19. Dalley, G., Migdal, J., Grimson, W.E.L.: ‘Background subtraction for temporally irregular dynamic textures’. Proc. IEEE Workshop on Applications of Computer Vision, 2008, pp. 1–7.
-
30)
-
25. Lee, D.: ‘Improved adaptive mixture learning for robust video background modeling’. Proc. IAPR Workshop on Machine Vision for Applications, 2002, pp. 443–446.
-
31)
-
31. Pilet, J., Strecha, C., Fua, P.: ‘Making background subtraction robust to sudden illumination changes’. Proc. European Conf. Computer Vision, 2008, pp. 567–580.
-
32)
-
2. Elhabian, S., El-Sayed, K., Ahmed, S.: ‘Moving object detection in spatial domain using background removal techniques-state-of-art’, Recent Pat. Comput. Sci., 2008, 1, (1), pp. 32–54 (doi: 10.2174/1874479610801010032).
-
33)
-
Z. Zivkovic ,
F. van der Heijden
.
Efficient adaptive density estimation per image pixel for the task of background subtraction.
Pattern Recognit. Lett.
-
34)
-
22. Zhong, B., Yao, H., Yuan, X.: ‘Local histogram of figure/ground segmentations for dynamic background subtraction’, EURASIP J. Adv. Signal Process., 2010, .
-
35)
-
8. Zhang, Y., Liang, Z., Hou, Z., Wang, H., Tan, M.: ‘An adaptive mixture Gaussian background model with online background reconstruction and adjustable foreground mergence time for motion segmentation’. Proc. IEEE Int. Conf. Industrial Technology, 2005, pp. 23–27.
-
36)
-
6. Stauffer, C., Grimson, W.E.L.: ‘Adaptive background mixture models for real-time tracking’. Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition, 1999, vol. 2, pp. 246–252.
-
37)
-
40. Vacavant, A., Chateau, T., Wilhelm, A., Lequièvre, L.: ‘A benchmark dataset for foreground/background extraction’. Proc. Computer Vision – ACCV 2012 Workshops: Background Models Challenge, 2012, pp. 291–300.
-
38)
-
15. KadewTraKuPong, P., Bowden, R.: ‘An improved adaptive background mixture model for real-time tracking with shadow detection’. Proc. European Workshop on Advanced Video Based Surveillance Systems, September 2001.
-
39)
-
7. Amintoosi, M., Farbiz, F., Fathy, M., Analoui, M., Mozayani, N.: ‘Qr decomposition-based algorithm for background subtraction’. Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing, 2007, vol. 1, pp. 1093–1096.
-
40)
-
10. Wang, Y., Liang, Y., Zhang, L., Pan, Q.: ‘Adaptive spatiotemporal background modeling’, IET Comput. Vis., 2012, 6, (5), pp. 451–458 (doi: 10.1049/iet-cvi.2010.0229).
-
41)
-
4. Cheng, J., Yang, J., Zhou, Y., Cui, Y.: ‘Flexible background mixture models for foreground segmentation’, Image Vis. Comput., 2006, 24, (5), pp. 473–482 (doi: 10.1016/j.imavis.2006.01.018).
-
42)
-
26. Lindstrom, J., Lindgren, F., Ltrstrom, K., Holst, J., Holst, U.: ‘Background and foreground modeling using an online EM algorithm’. Proc. IEEE Int. Workshop on Visual Surveillance, Faculty of Computing, Information Systems and Mathematics, 2006, pp. 9–16.
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