Machine Vision and Applications

, Volume 25, Issue 5, pp 1257–1269 | Cite as

Background modeling in the maritime domain

Special Issue Paper

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.

Keywords

Background subtraction  Dynamic background Maritime surveillance  Maritime dataset 

References

  1. 1.
    Ablavsky, V.: Background models for tracking objects in water. In: ICIP, vol. 3, pp. 125–128 (2003)Google Scholar
  2. 2.
    ATON: Autonomous Agents for On-Scene Networked Incident Management. http://cvrr.ucsd.edu/aton/testbed
  3. 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)Google Scholar
  4. 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)CrossRefGoogle Scholar
  5. 5.
    changedetection.net: Benchmark dataset. http://www.changedetection.net/
  6. 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)Google Scholar
  7. 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)Google Scholar
  8. 8.
    Cucchiara, R., Grana, C., Piccardi, M., Prati, A.: Detecting moving objects, ghosts, and shadows in video streams. PAMI 25(10), 1337–1342 (2003)CrossRefGoogle Scholar
  9. 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)Google Scholar
  10. 10.
    Doretto, G., Chiuso, A., Wu, Y.N., Soatto, S.: Dynamic textures. IJCV 51(2), 91–109 (2003)CrossRefMATHGoogle Scholar
  11. 11.
    Elgammal, A.M., Harwood, D., Davis, L.S.: Non-parametric model for background subtraction. In: ECCV, pp. 751–767 (2000)Google Scholar
  12. 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)CrossRefGoogle Scholar
  13. 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)Google Scholar
  14. 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)Google Scholar
  15. 15.
    He, Q., Chu, C.H.H.: Detection of reflecting surfaces by a statistical model. In: SPIE (2009)Google Scholar
  16. 16.
    Heikkila, M., Pietikainen, M.: A texture-based method for modeling the background and detecting moving objects. PAMI 28(4), 657–662 (2006)CrossRefGoogle Scholar
  17. 17.
    Jug Sequence: Dynamic background sequences. http://www.cs.bu.edu/groups/ivc/data.php
  18. 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)Google Scholar
  19. 19.
    MAR: Maritime Activity Recognition Dataset. http://labrococo.dis.uniroma1.it/MAR
  20. 20.
    Mittal, A., Paragios, N.: Motion-based background subtraction using adaptive kernel density estimation. In: CVPR, pp. 302–309 (2004)Google Scholar
  21. 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)Google Scholar
  22. 22.
    Oliver, N.M., Rosario, B., Pentland, A.P.: A bayesian computer vision system for modeling human interactions. PAMI 22(8), 831–843 (2000)CrossRefGoogle Scholar
  23. 23.
    OpenCV: Open Source Computer Vision. http://opencv.org
  24. 24.
    Piccardi, M.: Background subtraction techniques: a review. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp. 3099–3104 (2004)Google Scholar
  25. 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)Google Scholar
  26. 26.
    Sheikh, Y., Shah, M.: Bayesian object detection in dynamic scenes. In: CVPR, pp. 74–79 (2005)Google Scholar
  27. 27.
    Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: CVPR, vol. 2, pp. 246–252 (1999)Google Scholar
  28. 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)Google Scholar
  29. 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)Google Scholar
  30. 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)CrossRefGoogle Scholar
  31. 31.
    Toyama, K., Krumm, J., Brumitt, B., Meyers, B.: Wallflower: principles and practice of background maintenance. In: ICCV, vol. 1, pp. 255–261 (1999)Google Scholar
  32. 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)Google Scholar
  33. 33.
    Wallflower Sequence: Test Images for Wallflower Paper. http://research.microsoft.com/en-us/um/people/jckrumm/wallflower/testimages.htm
  34. 34.
    Wang, H., Suter, D.: Background subtraction based on a robust consensus method. In: ICPR, pp. 223–226 (2006)Google Scholar
  35. 35.
    Water surface sequence: Statistical Modeling of Complex Background for Foreground Object Detection. http://perception.i2r.a-star.edu.sg/bk_model/bk_index.html
  36. 36.
    Zhang, S., Yao, H., Liu, S.: Dynamic background modeling and subtraction using spatio-temporal local binary patterns. In: ICIP, pp. 1556–1559 (2008)Google Scholar
  37. 37.
    Zhang, S., Yao, H., Liu, S.: Dynamic background subtraction based on local dependency histogram. IJPRAI 23(7), 1397–1419 (2009)Google Scholar
  38. 38.
    Zhang, S., Yao, H., Liu, S.: Spatial-temporal nonparametric background subtraction in dynamic scenes. In: ICME, pp. 518–521 (2009)Google Scholar
  39. 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)Google Scholar
  40. 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)CrossRefMATHMathSciNetGoogle Scholar
  41. 41.
    Zhao, M., Bu, J., Chen, C.: Robust background subtraction in HSV color space. In: SPIE: Multimedia Systems and Applications, pp. 325–332 (2002)Google Scholar
  42. 42.
    Zhong, B., Yao, H., Shan, S., Chen, X., Gao, W.: Hierarchical background subtraction using local pixel clustering. In: ICPR, pp. 1–4 (2008)Google Scholar
  43. 43.
    Zhong, J., Sclaroff, S.: Segmenting foreground objects from a dynamic textured background via a robust Kalman filter. In: ICCV, pp. 44–50 (2003)Google Scholar
  44. 44.
    Zivkovic, Z.: Improved adaptive Gaussian mixture model for background subtraction. Int. Conf. Pattern Recognit. 2, 28–31 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Domenico D. Bloisi
    • 1
  • Andrea Pennisi
    • 1
  • Luca Iocchi
    • 1
  1. 1.Department of Computer, Control, and Management EngineeringSapienza University of RomeRomeItaly

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