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Machine Vision and Applications
Published in: Machine Vision and Applications 5/2014

01-07-2014 | Special Issue Paper

Background modeling in the maritime domain

Authors: Domenico D. Bloisi, Andrea Pennisi, Luca Iocchi

Published in: Machine Vision and Applications | Issue 5/2014

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Abstract

Maritime environment represents a challenging scenario for automatic video surveillance due to the complexity of the observed scene: waves on the water surface, boat wakes, and weather issues contribute to generate a highly dynamic background. Moreover, an appropriate background model has to deal with gradual and sudden illumination changes, camera jitter, shadows, and reflections that can provoke false detections. Using a predefined distribution (e.g., Gaussian) for generating the background model can result ineffective, due to the need of modeling non-regular patterns. In this paper, a method for creating a “discretization” of an unknown distribution that can model highly dynamic background such as water is described. A quantitative evaluation carried out on two publicly available datasets of videos and images, containing data recorded in different maritime scenarios, with varying light and weather conditions, demonstrates the effectiveness of the approach.
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Footnotes
Literature
1.
Ablavsky, V.: Background models for tracking objects in water. In: ICIP, vol. 3, pp. 125–128 (2003)
2.
3.
Bloisi, D., Iocchi, L.: Independent multimodal background subtraction. In: Proceedings of the Third International Conference on Computational Modeling of Objects Presented in Images: Fundamentals, Methods and Applications, pp. 39–44 (2012)
4.
Bloisi, D.D., Iocchi, L.: ARGOS—a video surveillance system for boat trafic monitoring in venice. Int. J. Pattern Recognit. Artif. Intell. 23(7), 1477–1502 (2009)CrossRef
5.
6.
Cheung, S., Kamath, C.: Robust techniques for background subtraction in urban traffic video. In: Visual Communications and Image Processing, vol. 5308, pp. 881–892 (2004)
7.
Cristani, M., Farenzena, M., Bloisi, D., Murino, V.: Background subtraction for automated multisensor surveillance: a comprehensive review. EURASIP J. Adv. Signal Process. 1–24 (2010)
8.
Cucchiara, R., Grana, C., Piccardi, M., Prati, A.: Detecting moving objects, ghosts, and shadows in video streams. PAMI 25(10), 1337–1342 (2003)CrossRef
9.
Dalley, G., Migdal, J., Grimson, W.: Background subtraction for temporally irregular dynamic textures. In: IEEE Workshop on Applications of Computer Vision, pp. 1–7 (2008)
10.
Doretto, G., Chiuso, A., Wu, Y.N., Soatto, S.: Dynamic textures. IJCV 51(2), 91–109 (2003)CrossRefMATH
11.
Elgammal, A.M., Harwood, D., Davis, L.S.: Non-parametric model for background subtraction. In: ECCV, pp. 751–767 (2000)
12.
Elhabian, S.Y., El-Sayed, K.M., Ahmed, S.H.: Moving object detection in spatial domain using background removal techniques—state-of-art. Recent Pat. Comput. Sci. 1, 32–54 (2008)CrossRef
13.
Godbehere, A.B., Matsukawa, A., Goldberg, K.: Visual tracking of human visitors under variable-lighting conditions for a responsive audio art installation. In: American Control Conference (ACC), pp. 4305–4312 (2012)
14.
Goyette, N., Jodoin, P.M., Porikli, F., Konrad, J., Ishwar, P.: changedetection.net: a new change detection benchmark dataset. In: Proceedings of IEEE Workshop on Change Detection at CVPR12 (2012)
15.
He, Q., Chu, C.H.H.: Detection of reflecting surfaces by a statistical model. In: SPIE (2009)
16.
Heikkila, M., Pietikainen, M.: A texture-based method for modeling the background and detecting moving objects. PAMI 28(4), 657–662 (2006)CrossRef
17.
18.
Kaewtrakulpong, P., Bowden, R.: An improved adaptive background mixture model for realtime tracking with shadow detection. In: Proceedings of 2nd European Workshop on Advanced Video Based Surveillance Systems, pp. 135–144 (2001)
19.
20.
Mittal, A., Paragios, N.: Motion-based background subtraction using adaptive kernel density estimation. In: CVPR, pp. 302–309 (2004)
21.
Noriega, P., Bernier, O.: Real time illumination invariant background subtraction using local kernel histograms. In: Proceedings of BMVC, pp. 100.1-100.10 (2006)
22.
Oliver, N.M., Rosario, B., Pentland, A.P.: A bayesian computer vision system for modeling human interactions. PAMI 22(8), 831–843 (2000)CrossRef
23.
24.
Piccardi, M.: Background subtraction techniques: a review. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp. 3099–3104 (2004)
25.
Rankin, A.L., Matthies, L.H., Huertas, A.: Daytime water detection by fusing multiple cues for autonomous off-road navigation. In: 24th Army Science Conference, vol. 9 (2004)
26.
Sheikh, Y., Shah, M.: Bayesian object detection in dynamic scenes. In: CVPR, pp. 74–79 (2005)
27.
Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: CVPR, vol. 2, pp. 246–252 (1999)
28.
Stenger, B., Ramesh, V., Paragios, N., Coetzee, F., Buhmann, J.: Topology free hidden markov models: application to background modeling. In: ICCV, vol. 1, pp. 294–301 (2001)
29.
Tavakkoli, A., Nicolescu, M., Bebis, G.: Robust recursive learning for foreground region detection in videos with quasi-stationary backgrounds. In: ICPR, pp. 315–318 (2006)
30.
Tian, Y., Feris, R.S., Liu, H., Hampapur, A., Sun, M.T.: Robust detection of abandoned and removed objects in complex surveillance videos. IEEE Trans. Syst. Man Cybern. Part C 41(5), 565–576 (2011)CrossRef
31.
Toyama, K., Krumm, J., Brumitt, B., Meyers, B.: Wallflower: principles and practice of background maintenance. In: ICCV, vol. 1, pp. 255–261 (1999)
32.
Vacavant, A., Chateau, T., Wilhelm, A., Lequivre, L.: A benchmark dataset for outdoor foreground/background extraction. In: ACCV 2012, Workshop: Background Models, Challenge (2012)
33.
34.
Wang, H., Suter, D.: Background subtraction based on a robust consensus method. In: ICPR, pp. 223–226 (2006)
35.
36.
Zhang, S., Yao, H., Liu, S.: Dynamic background modeling and subtraction using spatio-temporal local binary patterns. In: ICIP, pp. 1556–1559 (2008)
37.
Zhang, S., Yao, H., Liu, S.: Dynamic background subtraction based on local dependency histogram. IJPRAI 23(7), 1397–1419 (2009)
38.
Zhang, S., Yao, H., Liu, S.: Spatial-temporal nonparametric background subtraction in dynamic scenes. In: ICME, pp. 518–521 (2009)
39.
Zhang, S., Yao, H., Liu, S., Chen, X., Gao, W.: A covariance-based method for dynamic background subtraction. In: ICPR, pp. 1–4 (2008)
40.
Zhao, J., Xu, X., Ding, X.: New goodness of fit tests based on stochastic EDF. Commun. Stat. Theory Methods 39(6), 1075–1094 (2010)CrossRefMATHMathSciNet
41.
Zhao, M., Bu, J., Chen, C.: Robust background subtraction in HSV color space. In: SPIE: Multimedia Systems and Applications, pp. 325–332 (2002)
42.
Zhong, B., Yao, H., Shan, S., Chen, X., Gao, W.: Hierarchical background subtraction using local pixel clustering. In: ICPR, pp. 1–4 (2008)
43.
Zhong, J., Sclaroff, S.: Segmenting foreground objects from a dynamic textured background via a robust Kalman filter. In: ICCV, pp. 44–50 (2003)
44.
Zivkovic, Z.: Improved adaptive Gaussian mixture model for background subtraction. Int. Conf. Pattern Recognit. 2, 28–31 (2004)
Metadata
Title
Background modeling in the maritime domain
Authors
Domenico D. Bloisi
Andrea Pennisi
Luca Iocchi
Publication date
01-07-2014
Publisher
Springer Berlin Heidelberg
Published in
Machine Vision and Applications / Issue 5/2014
Print ISSN: 0932-8092
Electronic ISSN: 1432-1769
DOI
https://doi.org/10.1007/s00138-013-0554-5

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