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Moving Cast Shadows Detection Methods for Video Surveillance Applications

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Wide Area Surveillance

Part of the book series: Augmented Vision and Reality ((Augment Vis Real,volume 6))

Abstract

Moving cast shadows are a major concern in today’s performance from broad range of many vision-based surveillance applications because they highly difficult the object classification task. Several shadow detection methods have been reported in the literature during the last years. They are mainly divided into two domains. One usually works with static images, whereas the second one uses image sequences, namely video content. In spite of the fact that both cases can be analogously analyzed, there is a difference in the application field. The first case, shadow detection methods can be exploited in order to obtain additional geometric and semantic cues about shape and position of its casting object (‘shape from shadows’) as well as the localization of the light source. While in the second one, the main purpose is usually change detection, scene matching or surveillance (usually in a background subtraction context). Shadows can in fact modify in a negative way the shape and color of the target object and therefore affect the performance of scene analysis and interpretation in many applications. This chapter wills mainly reviews shadow detection methods as well as their taxonomies related with the second case, thus aiming at those shadows which are associated with moving objects (moving shadows).

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Notes

  1. 1.

    http://news.bbc.co.uk/2/hi/uk_news/6108496.stm

  2. 2.

    http://epic.org/privacy/surveillance/

  3. 3.

    Frame-level methods are included, despite the fact that they are not widely used, in order to obtain a thorough review of the methods.

  4. 4.

    Note that for a quantitative evaluation a ground truth is necessary, the sequences as well as their ground truth are publicly accessible in the listed links.

  5. 5.

    http://cvrr.ucsd.edu/aton/shadow/

  6. 6.

    http://vision.gel.ulaval.ca/~CastShadows/

  7. 7.

    http://www.cvc.uab.es/~aamato/Shadows_Detection/;

    http://www.cvg.rdg.ac.uk/PETS2009/a.html

References

  1. Amato, A., Mozerov, M., Roca, X., Gonzàlez, J.: Robust real-time background subtraction based on local neighborhood patterns. In: EURASIP Journal on Advances in Signal Processing, pp. 1–7, June 2010

    Google Scholar 

  2. Amato, A., Mozerov, M.G., Bagdanov, A.D., Gonzàlez, J.: Accurate moving cast shadow suppression based on local color constancy detection. Image Process. IEEE Trans. 20(10), 2954–2966 (2011)

    Article  Google Scholar 

  3. Barnich, O., Van Droogenbroeck, M.: Vibe: a universal background subtraction algorithm for video sequences. IEEE TIP 20(6), 1709–1724 (2011)

    Google Scholar 

  4. Brutzer, S., Hoferlin, B., Heidemann, G.: Evaluation of background subtraction techniques for video surveillance. In: IEEE CVPR’11, pp. 1937–1944, June 2011

    Google Scholar 

  5. Bugeau, A., Perez, P.: Detection and segmentation of moving objects in highly dynamic scenes. In: IEEE CVPR’07, pp. 1–6, June 2008

    Google Scholar 

  6. Caputo, A.: Digital Video Surveillance and Security. Butterworth-Heinemann, Burlington (2010)

    Google Scholar 

  7. Cezar Silveira Jacques, J., Rosito Jung, C., Musse, S.R.: A background subtraction model adapted to illumination changes. In: Image Processing, 2006 IEEE International Conference on, pp. 1817–1820, October 2006

    Google Scholar 

  8. Chang, C.-J., Hu, W.-F., Hsieh, J.-W., Chen, Y.-S.: Shadow elimination for effective moving object detection with gaussian models. In: Pattern Recognition, 2002. Proceedings. 16th International Conference on, vol. 2, pp. 540–543, 2002

    Google Scholar 

  9. Chen, Y., Chen, C., Huang, C., Hung, Y.: Efficient hierarchical method for background subtraction. Pattern Recognit. 40(10), 2706–2715 (2007)

    Article  MATH  Google Scholar 

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

    MathSciNet  Google Scholar 

  11. Colombari, A., Fusiello, A., Murino, V.: Patch-based background initialization in heavily cluttered video. IEEE TIP 19(4), 926–933 (2010)

    Google Scholar 

  12. Cucchiara, R., Grana, C., Piccardi, M., Prati, A., Sirotti, S.: Improving shadow suppression in moving object detection with HSV color information. In: Intelligent Transportation Systems, 2001. Proceedings. 2001 IEEE, pp. 334–339, 2001

    Google Scholar 

  13. Elgammal, A., Harwood, D., Davis, L.S.: Nonparametric background model for background subtraction. In: ECCV’00, pp. 751–767, Dublin, 2000

    Google Scholar 

  14. Forsyth, D., Ponce, J.: Computer Vision: A Modern Approach. Prentice Hall, Upper Saddle River (2002)

    Google Scholar 

  15. Fung, G.S.K., Yung, N.H.C., Pang, G.K.H., Lai, A.H.S.: Effective moving cast shadow detection for monocular color image sequences. In: Image Analysis and Processing, 2001. Proceedings. 11th International Conference on, pp. 404–409, 2001

    Google Scholar 

  16. Gavrila, D.M.: The visual analysis of human movement: a survey. Comput. Vis. Image Underst. 73, 82–98 (1999)

    Article  MATH  Google Scholar 

  17. Grest, D., Frahm, J.M., Koch, R.: A color similarity measure for robust shadow removal in real time. In: Vision, Modeling and Visualization, pp. 253–260, 2003

    Google Scholar 

  18. Haritaoglu, I., Harwood, D., Davis, L.S: W4: real-time surveillance of people and their activities. IEEE TPAMI 22(8), 809–830 (2000)

    Article  Google Scholar 

  19. Heikkila, J., Silven, O.: A real-time system for monitoring of cyclists and pedestrians. In: Proceedings of the Second IEEE Workshop on Visual Surveillance, pp. 74–81, Washington, DC, 1999. IEEE Computer Society, Washington (1999)

    Google Scholar 

  20. Horprasert, T., Harwood, D., Davis, L.S.: A statistical approach for real-time robust background subtraction and shadow detection. In: ICCV Frame-Rate WS. IEEE, 1999

    Google Scholar 

  21. Hsieh, J.-W., Yu, S.-H., Chen, Y.-S., Hu, W.-F.: A shadow elimination method for vehicle analysis. In: Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, vol. 4, pp. 372–375, 2004

    Google Scholar 

  22. Huang, J.-B., Chen, C.-S.: Moving cast shadow detection using physics-based features. In: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on, pp. 2310–2317, 2009

    Google Scholar 

  23. Huerta, I., Holte, M., Moeslund, T.B., Gonzàlez, J.: Detection and removal of chromatic moving shadows in surveillance scenarios. In: ICCV2009, Kyoto, 2009

    Google Scholar 

  24. Huerta, I., Amato, A., Roca, F.X., Gonzàlez, J.: Multiple cues fusion for robust motion segmentation using background subtraction. Neurocomputing, Elsevier, (2011, in press)

    Google Scholar 

  25. Jabri, H.W.S., Duric, Z., Rosenfeld, A.: Detection and location of people in video images using adaptive fusion of color and edge information. In: 15th ICPR, vol. 4, pp. 627–630, Barcelona, Sept 2000

    Google Scholar 

  26. Javed, O., Shafique, K., Shah, M.: A hierarchical approach to robust background subtraction using color and gradient information. In: Proceedings of the Workshop on Motion and Video Computing (MOTION’02), p. 22, Orlando, 2002

    Google Scholar 

  27. Karaman, M., Goldmann, L., Yu, D., Sikora, T.: Comparison of static background segmentation methods. In: VCIP ’05, Beijing, July 2005

    Google Scholar 

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

    Article  Google Scholar 

  29. Leone, A., Distante, C.: Shadow detection for moving objects based on texture analysis. Pattern Recognit. 40(4), 1222–1233 (2007)

    Article  MATH  Google Scholar 

  30. Lin, W. (ed.): Video Surveillance. InTech, (2011). ISBN 978-953-307-436-8

    Google Scholar 

  31. Li, L., Huang, W., Gu, I.Y.-H., Tian, Q.: Statistical modeling of complex backgrounds for foreground object detection. IEEE TIP 13(11), 1459–1472 (2004)

    Google Scholar 

  32. Liu, Z., Huang, K., Tan, T., Wang, L.: Cast shadow removal combining local and global features. In: Computer Vision and Pattern Recognition, 2007. CVPR ’07. IEEE Conference on, pp. 1–8, June 2007

    Google Scholar 

  33. Lopez-Rubio, E., Luque-Baena, R.M., Dominguez, E.: Foreground detection in video sequences with probabilistic self-organizing maps. Int. J. Neural Syst. 21(3), 225–246 (2011)

    Article  Google Scholar 

  34. Maddalena, L., Petrosino, A.: A self-organizing approach to background subtraction for visual surveillance applications. IEEE TIP 17(7), 1168–1177 (2008)

    MathSciNet  Google Scholar 

  35. Mahadevan, V., Vasconcelos, N.: Spatiotemporal saliency in dynamic scenes. IEEE TPAMI 32(1), 171–177 (2010)

    Article  Google Scholar 

  36. Martel-Brisson, N., Zaccarin, A.: Learning and removing cast shadows through a multidistribution approach. Pattern Anal. Mach. Intell. IEEE Trans. 29(7), 1133–1146 (2007)

    Article  Google Scholar 

  37. Martel-Brisson, N., Zaccarin, A.: Kernel-based learning of cast shadows from a physical model of light sources and surfaces for low-level segmentation. In: CVPR08, pp. 1–8, 2008

    Google Scholar 

  38. McIvor, A.: Background subtraction techniques. In: Proceedings of Image and Vision Computing, Auckland, 2000

    Google Scholar 

  39. McKenna, S.J., Jabri, S., Duric, Z., Rosenfeld, A., Wechsler, h.: Tracking groups of people. Comput. Vis. Image Underst. 80(1), 42–56 (2000)

    Article  MATH  Google Scholar 

  40. Mittal, A., Paragios, N.: Motion-based background subtraction using adaptive kernel density estimation. In: Proceedings of CVPR’04, vol. 2, pp. 302–309, Washington DC, July 2004

    Google Scholar 

  41. Moeslund, T.B., Granum, E.: A survey of computer vision-based human motion capture. Comput. Vis. Image Underst. 81(3), 231–268 (2001)

    Article  MATH  Google Scholar 

  42. Nadimi, S., Bhanu, B.: Physical models for moving shadow and object detection in video. Pattern Anal. Mach. Intell. IEEE Trans. 26(8), 1079–1087 (2004)

    Article  Google Scholar 

  43. Obinata, G., Dutta, A.: Vision Systems: Segmentation and Pattern Recognition. I-Tech Education and Publishing, Vienna (2007)

    Book  Google Scholar 

  44. Patwardhan, K.A., Sapiro, G., Morellas, V.: Robust foreground detection in video using pixel layers. IEEE TPAMI 30(4), 746–751 (2008)

    Article  Google Scholar 

  45. Piccardi, M.: Background subtraction techniques: a review. In: IEEE International Conference on Systems, Man and Cybernetics, vol. 4, pp. 3099–3104, The Hague, 2004

    Google Scholar 

  46. Porikli, F., Thornton, J.: Shadow flow: a recursive method to learn moving cast shadows. In: Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on, vol. 1, pp. 891–898, 2005

    Google Scholar 

  47. Prati, A., Mikic, I., Trivedi, M.M., Cucchiara, R.: Detecting moving shadows: algorithms and evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 25(7), 918–923 (2003)

    Article  Google Scholar 

  48. Rosin, P., Ellis, T.: Image difference threshold strategies and shadow detection. In: Proceedings of British Machine Vision Conference, pp. 347–356. BMVA Press, Surrey (1995)

    Google Scholar 

  49. Salvador, E., Cavallaro, A., Ebrahimi, T.: Shadow identification and classification using invariant color models. In: Acoustics, Speech, and Signal Processing, 2001. Proceedings. (ICASSP ’01). 2001 IEEE International Conference on, vol. 3, pp. 1545–1548, 2001

    Google Scholar 

  50. Salvador, E., Cavallaro, A., Ebrahimi, T.: Spatio-temporal shadow segmentation and tracking. In: Proceedings of Visual Communications and Image Processing, pp. 389–400, 2003

    Google Scholar 

  51. Salvador, E., Cavallaro, A., Ebrahimi, T.: Cast shadow segmentation using invariant color features. Comput. Vis. Image Underst. 95(2), 238–259 (2004)

    Article  Google Scholar 

  52. Sanin, A., Sanderson, C., Lovell, B.C.: Shadow detection: a survey and comparative evaluation of recent methods. Pattern Recogn. 45(4), 1684–1695 (2012)

    Article  Google Scholar 

  53. SanMiguel, J.C., Martinez, J.M.: On the evaluation of background subtraction algorithms without ground-truth. In: Advanced Video and Signal Based Surveillance (AVSS), 2010 Seventh IEEE International Conference on, pp. 180–187, Sept 2010

    Google Scholar 

  54. Sheikh, Y., Shah, M.: Bayesian modeling of dynamic scenes for object detection. IEEE TPAMI 27(11), 1778–1792 (2005)

    Article  Google Scholar 

  55. Shen, J.: Motion detection in color image sequence and shadow elimination. Vis. Commun. Image Process. 5308, 731–740 (2004)

    Google Scholar 

  56. Siala, K., Chakchouk, M., Chaieb, F., Besbes, O.: Moving shadow detection with support vector domain description in the color ratios space. In: Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, vol. 4, pp. 384–387, August 2004

    Google Scholar 

  57. Spagnolo, P., Orazio, T.D., Leo, M., Distante, A.: Moving object segmentation by background subtraction and temporal analysis. Image Vis. Comput. 24(5), 411–423 (2006)

    Article  Google Scholar 

  58. Stauder, J., Mech, R., Ostermann, J.: Detection of moving cast shadows for object segmentation. IEEE Trans. Multimedia 1(1), 65–76 (1999)

    Article  Google Scholar 

  59. Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: IEEE CVPR’99, vol. 1, pp. 22–29, Ft. Collins, 1999

    Google Scholar 

  60. Stauffer, C., Eric, W., Grimson, L.: Learning patterns of activity using real-time tracking. IEEE TPAMI 22(8), 747–757 (2000)

    Article  Google Scholar 

  61. Toth, D., Stuke, I., Wagner, A., Aach, T.: Detection of moving shadows using mean shift clustering and a significance test. In: International Conference on Pattern Recognition (ICPR 2004), vol. 4, pp. 260–263, 2004

    Google Scholar 

  62. Toyama, K., Krumm, J., Brumitt, B., Meyers, B.: Wallflower: principles and practice of background maintenance. In: Proceedings of ICCV’99, vol. 1, pp. 255–261, Kerkyra, 1999

    Google Scholar 

  63. Ullah, H., Ullah, M., Uzair, M., Rehman, F.: Comparative study: the evaluation of shadow detection methods. Int. J. Video Image Process. Netw. Secur. 10(2), 1–7

    Google Scholar 

  64. Wang, L., Hu, W., Tan, T.: Recent developments in human motion analysis. Pattern Recognit. 36(3), 585–601 (2003)

    Article  Google Scholar 

  65. Wang, L., Hu, W., Tan, T.: Recent developments in human motion analysis. Pattern Recognit. 36(3), 585–601 (2003)

    Article  Google Scholar 

  66. Weiss, Y.: Deriving intrinsic images from image sequences. In: Proceedings of ICCV’01, vol. 02, pp. 68–75, Vancouver, 2001

    Google Scholar 

  67. Wren, C.R., Azarbayejani, A., Darrell, T., Pentland, A.P: Pfinder: real-time tracking of the human body. IEEE TPAMI 19(7), 780–785 (1997)

    Article  Google Scholar 

  68. Xu, D., Li, X., Liu, Z., Yuan, Y.: Cast shadow detection in video segmentation. Pattern Recognit. Lett. 26(1), 91–99 (2005)

    Article  Google Scholar 

  69. Yang, M.-T., Lo, K.-H., Chinag, C.-C., Tai, W.-K.: Moving cast shadow detection by exploiting multiple cues. Image Process. IET 2(2), 95–104 (2008)

    Google Scholar 

  70. Yao, J., Odobez, J.M.: Multi-layer background subtraction based on color and texture. In: IEEE CVPR’07, pp. 17–22, Minneapolis, June 2007

    Google Scholar 

  71. Yuan, C., Yang, C., Xu, Z.: Simple vehicle detection with shadow removal at intersection. In: Proceedings of the 2010 Second International Conference on Multi-Media and Information Technology, volume 02 of MMIT ’10, pp. 188–191. IEEE Computer Society, 2010

    Google Scholar 

  72. Zhang, W., Wu, Q.M.J., Fang, X.: In: Obinata, G., Dutta, A. (eds.): Vision Systems: Segmentation and Pattern Recognition. Moving Cast Shadow Detection. InTech, (2007)

    Google Scholar 

  73. Zivkovic, Z.: Improved adaptive gaussian mixture model for background subtraction. In: Proceedings of ICPR’04, vol. 2, pp. 23–26, August 2004

    Google Scholar 

  74. Zivkovic, Z., Heijden, F.: Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recognit. Lett. 27(7), 773–780 (2006)

    Article  Google Scholar 

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Acknowledgments

Consolider-Ingenio 2010: MIPRCV (CSD200700018); Avanza I+D ViCoMo (TSI-020400-2009-133) and DiCoMa (TSI-020400-2011-55); along with the Spanish projects TIN2009-14501-C02-01 and TIN2009-14501-C02-02.

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Authors and Affiliations

  1. Computer Vision Center, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain

    Ariel Amato, Mikhail G. Mozerov, F. Xavier Roca & Jordi Gonzàlez

  2. Institut de Robòtica i Informàtica industrial, Universitat Politècnica de Catalunya, Barcelona, Spain

    Ivan Huerta

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  1. Ariel Amato
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  2. Ivan Huerta
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  3. Mikhail G. Mozerov
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Correspondence to Ariel Amato .

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  1. Electrical and Computer Engineering, University of Dayton, Ohio, USA

    Vijayan K. Asari

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Amato, A., Huerta, I., Mozerov, M.G., Roca, F.X., Gonzàlez, J. (2014). Moving Cast Shadows Detection Methods for Video Surveillance Applications. In: Asari, V. (eds) Wide Area Surveillance. Augmented Vision and Reality, vol 6. Springer, Berlin, Heidelberg. https://doi.org/10.1007/8612_2012_3

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