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Multimedia Systems

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29-08-2018 | Regular Paper

Moving object detection using edges of residuals under varying illuminations

Author: Wonjun Kim

Published in: Multimedia Systems | Issue 3/2019

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Abstract

This paper presents a new method for moving object detection under varying illuminations. The key idea of the proposed method is to reconstruct moving objects from edges computed on the result of frame differencing, the so-called edges of residuals. This scheme forces lighting variations to be efficiently suppressed in the gradient space while preserving the boundary of moving objects. The inner areas of moving objects are subsequently reconstructed by utilizing image gradients of the original frame, which are masked by edges of residuals, in a least-square sense. One important advantage of the proposed method is to uniformly highlight moving objects regardless of their scales. Experimental results on various databases demonstrate that the proposed method is effective for moving object detection under diverse lighting conditions.

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Wang, K., Liu, Y., Guo, C., Wang, F.-Y.: A multi-view learning approach to foreground detection for traffic surveillance applications. IEEE Trans. Veh. Technol. 65(6), 4144–4158 (2016)CrossRef

Tian, Y., Wang, Y., Hu, Z., Huang, T.: Selective eigenbackground for background modeling and subtraction in crowded scenes. IEEE Trans. Circuits Syst. Video Technol. 23(11), 1849–1864 (2013)CrossRef

Baysal, S., Duygulu, P.: Directional coherence-based spatiotemporal descriptor for object detection in static and dynamic scenes. IEEE Trans. Circuits Syst. Video Technol. 26(7), 1350–1362 (2016)CrossRef

Tian, Y., Cao, L., Liu, Z., Zhang, Z.: Hierarchical filtered motion for action recognition in crowded videos. IEEE Trans. Syst. Man Cybern.-Part C Appl. Rev 42(3), 313–323 (2012)CrossRef

Zhang, T., Wiliem, A., Hemson, G., Lovell, B.C.: Detecting kangaroos in the wild: the first step towards automated animal surveillance. In: Proc. IEEE Int. Conf. Acoust., Speech Signal Process, pp. 1961–1965, (2018)

Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 2, 246–252 (1999)

Zivkovic, Z.: Improved adaptive Gaussian mixture model for background subtraction. In: Proc. 17th Int. Conf. Pattern Recognit. (ICPR), vol. 2. pp. 28–31, (2004)

White, B., Shah, M.: Automatically tuning background subtraction parameters using particle swarm optimization. In: Proc. IEEE Conf. Multimedia Expo (ICME), pp. 1826–1829, (2007)

Haines, T.S.F., Xiang, T.: Background subtraction with Dirichlet process mixture models. IEEE Trans. Pattern Anal. Mach. Intell. 36(4), 670–683 (2014)CrossRef

10.

Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.: Real-time foreground–background segmentation using codebook model. Real-Time Imaging 11(3), 172–185 (2005)CrossRef

11.

Guo, J.-M., Lium, Y.-F., Hsia, C.-H., Hsu, C.-S.: Hierarchical method for foreground detection using codebook model. IEEE Trans. Circuits Syst. Video Technol. 21(6), 804–815 (2011)CrossRef

12.

Zeng, Z., Jia, J., Zhu, Z., Yu, D.: Adaptive maintenance scheme for codebook-based dynamic background subtraction. Comput. Vis. Image Underst. 152, 58–66 (2016)CrossRef

13.

Barnich, O., Droogenbroeck, M.V.: ViBe: a universal background subtraction algorithm for video sequences. IEEE Trans. Image Process. 20(6), 1709–1724 (2011)MathSciNetCrossRefMATH

14.

St-Charles, P.L., Bilodeau, G.A., Bergevin, R.: SuBSENSE: a universal change detection method with local adaptive sensitivity. IEEE Trans. Image Process. 24(1), 359–373 (2015)MathSciNetCrossRefMATH

15.

Maddalena, L., Petrosino, A.: The SOBS algorithm: what are the limits ? In: Proc. IEEE Comput. Vis. Pattern Recognit. Workshop (CVPRW), pp. 21–26 (2012)

16.

Zhou, X., Yang, C., Yu, W.: Moving object detection by detecting contiguous outliers in the low-rank representation. IEEE Trans. Pattern Anal. Mach. Intell. 35(3), 597–610 (2013)CrossRef

17.

Sobral, A., Javed, S., Jung, S. K., Bouwmans, T., Zahzah, E. H.: Online stochastic tensor decomposition for background subtraction in multispectral video sequences. In: Proc. IEEE Int. Conf. Comput. Vis. Workshop (ICCVW), pp. 946–953 (2015)

18.

Li, L., Wang, P., Hu, Q., Cai, S.: Efficient background modeling based on sparse representation and outlier iterative removal. IEEE Trans. Circuits Syst, Video Technol. 26(2), 278–289 (2016)CrossRef

19.

Hu, W., Li, X., Zhang, X., Shi, X., Maybank, S., Zhang, Z.: Incremental tensor subspace learning and its applications to foreground segmentation and tracking. Int. J. Comput. Vis. 91(3), 303–327 (2011)CrossRefMATH

20.

Cao, W., Wang, Y., Sun, J., Meng, D., Yang, C., Cichocki, A., Xu, Z.: Total variation regularized tensor RPCA for background subtraction from compressive measurements. IEEE Trans. Image Process. 25(9), 4075–4090 (2016)MathSciNetCrossRefMATH

21.

Horn, B.K.P.: Determining lightness from an image. Comput. Gr. Image Process. 3, 277–299 (1974)CrossRef

22.

Kim, C., Hwang, J.-N.: Object-based video abstraction for video surveillance systems. IEEE Trans. Circuits Syst. Video Technol. 12(12), 1128–1138 (2002)CrossRef

23.

He, K., Sun, J., Tang, X.: Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409 (2013)CrossRef

24.

Canny, J.F.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 6, 679–698 (1986)CrossRef

25.

Tumblin, J., Agrawal, A., Raskar, R.: why I want a gradient camera ? In: Proc. IEEE Comput. Vis. Pattern Recognit. (CVPR), pp. 103–110 (2005)

26.

Agrawal, A., Chellappa, R., Raskar, R.: An algebraic approach to surface reconstruction from gradient fields. In: Proc. IEEE Int. Conf. Comput. Vis. (ICCV), pp. 174–181 (2005)

27.

IEEE Workshop on Performance Evaluation of Tracking and Surveillance. [Online]. Available: http://ftp.pets.rdg.ac.uk/pub/pets2001. Accessed 9 Dec 2001

28.

Davis, J., Sharma, V.: Background subtraction using contour based fusion of thermal and visible imagery. Comput. Vis. Image Underst. 106(2), 162–182 (2007)CrossRef

29.

Agarwal, S., Awan, A., Roth, D.: Learning to detect objects in images via a sparse, part-based representation. IEEE Trans. Pattern Anal. Mach. Intell. 26(11), 1475–1490 (2004)CrossRef

30.

Cheng, L., Gong, M., Schuurmans, D., Caelli, T.: Real-time discriminative background subtraction. IEEE Trans. Image Process. 20(5), 1401–1414 (2011)MathSciNetCrossRefMATH

31.

Kim, W., Kim, Y.: Background subtraction using illumination-invariant structural complexity. IEEE Signal Process. Lett. 23(5), 634–638 (2016)CrossRef

32.

Lim, L. A., Keles, H.Y.: Foreground segmentation using a triplet convolutional neural network for multiscale feature encoding. arXiv:1801.02225, (2018)

33.

Wang, Y., Jodoin, P.-M., Porikli, F., Konrad, J., Benezeth, Y., Ishwar, P.: CDnet 2014: an expanded change detection benchmark dataset. In: Proc. IEEE Comput. Vis. Pattern Recognit. Workshops (CVPRW), pp. 387–394 (2014)

34.

Ho, J., Lim, J., Yang, M-H., Kriegman, D.: “Integrating surface normal vectors using fast marching method. In: Proc. Eur. Conf. Comput. Vis. (ECCV), pp. 387–394 (2006)

35.

Bhat, P., Curless, B., Cohen, M., Zitnick, C. L.: Fourier analysis of the 2D screened Poisson equation for gradient domain problems. In: Proc. Eur. Conf. Comput. Vis. (ECCV). pp. 114–128, (2008)

Title: Moving object detection using edges of residuals under varying illuminations
Author: Wonjun Kim
Publication date: 29-08-2018
Publisher: Springer Berlin Heidelberg
Published in: Multimedia Systems / Issue 3/2019
Print ISSN: 0942-4962
Electronic ISSN: 1432-1882
DOI: https://doi.org/10.1007/s00530-018-0593-x

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