Advertisement

Machine Vision and Applications

, Volume 25, Issue 5, pp 1133–1144 | Cite as

Mixture of Merged Gaussian Algorithm using RTDENN

  • Manuel Alvar
  • Andrea Rodriguez-Calvo
  • Alvaro Sanchez-Miralles
  • Alvaro Arranz
Special Issue Paper

Abstract

Computer vision has been a widely developed research area in the last years, and it has been used for a broad range of applications, including surveillance systems. In the pursuit of an autonomous and smart motion detection system, a reliable segmentation algorithm is required. The main problems of present segmentation solutions are their high execution time and the lack of robustness against changes in the environment due to variations in lighting, shadows, occlusions or the movement of secondary objects. This paper proposes a new algorithm named Mixture of Merged Gaussian Algorithm (MMGA) that aims to achieve a substantial improvement in execution speed to enable real-time implementation, without compromising the reliability and accuracy of the segmentation. The MMGA is based on the combination of a probabilistic model for the background, similar to the Mixture of Gaussian Model (MGM), with the learning processes of Real-Time Dynamic Ellipsoidal Neural Networks (RTDENN) for the update of the model. The proposed algorithm has been tested for different videos and compared to the MGM and SDGM algorithms. Results show a reduction of 30 to 50 % in execution times. Furthermore, the segmentation is more robust against the effect of noise and adapts faster to lighting changes.

Keywords

Computer vision Video surveillance Object detection Image motion analysis Object segmentation Motion detection Mixture of Gaussian model Real-Time Dynamic Ellipsoidal neural network 

References

  1. 1.
    Alvar, M., Sánchez, A., Arranz, A.: Fast background subtraction using static and dynamic gates. Artif. Intell. Rev. 2011, 1–16 (2012). doi: 10.1007/s10462-011-9301-3 Google Scholar
  2. 2.
    Chen, S., Zhang, J., Li, Y., Zhang, J.: A hierarchical model incorporating segmented regions and pixel descriptors for video background subtraction. IEEE Trans. Ind. Inf. 8(1), 118–127 (2012). doi: 10.1109/TII.2011.2173202 CrossRefGoogle Scholar
  3. 3.
    Cucchiara, R., Grana, C., Piccardi, M., Prati, A.: Detecting moving objects, ghosts, and shadows in video streams. IEEE Trans. Pattern Anal. Mach. Intell. 25(10), 1337–1342 (2003)CrossRefGoogle Scholar
  4. 4.
    Del Rose, M., Wagner, C.: Survey on classifying human actions through visual sensors. Artif. Intell. Rev. 2011, 1–11 (2011). doi: 10.1007/s10462-011-9232-z Google Scholar
  5. 5.
    Dockstader, S., Tekalp, A.: Real-time object tracking and human face detection in cluttered scenes. In: Image and Video Communications and Processing, 2000. Proceedings of the Society of Photo-Optical Instrumentation Engineers (SPIE), vol 3974, pp 957–968 (2000)Google Scholar
  6. 6.
    Hampapur, A., Brown, L., Connell, J., Pankanti, S., Senior, A., Tian, Y.: Smart surveillance: applications, technologies and implications. In: Proceedings of the 2003 Joint Conference of the Fourth International Conference on Information, Communications and Signal Processing, 2003 and the Fourth Pacific Rim Conference on Multimedia, vol. 2, pp. 1133–1138 (2003). doi: 10.1109/ICICS.2003.1292637
  7. 7.
    Heikkila, M., Pietikainen, M.: A texture-based method for modeling the background and detecting moving objects. IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 657–662 (2006). doi: 10.1109/TPAMI.2006.68 CrossRefGoogle Scholar
  8. 8.
    Horprasert, T., Harwood, D., Davis, LS.: A statistical approach for real-time robust background subtraction and shadow detection, pp 1–19 (1999)Google Scholar
  9. 9.
    Howarth, R.: Spatial models for wide-area visual surveillance: computational approaches and spatial building-blocks. Artif. Intell. Rev. 23, 97–155 (2005). doi: 10.1007/s10462-004-4103-5 CrossRefGoogle Scholar
  10. 10.
    Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.: Real-time foregroundbackground segmentation using codebook model. Real-Time Imag. 11(3), 172–185 (2005)CrossRefGoogle Scholar
  11. 11.
    Ko, T.: A survey on behavior analysis in video surveillance for homeland security applications. In: Applied Imagery Pattern Recognition Workshop, 2008. AIPR ’08. 37th IEEE, pp. 1–8 (2008) doi: 10.1109/AIPR.2008.4906450
  12. 12.
    Lipton, A.J., Fujiyoshi, H., Patil, R.S.: Moving target classification and tracking from real-time video. In: Proceedings of Fourth IEEE Workshop on Applications of Computer Vision, 1998. WACV ’98, pp 8–14 (1998)Google Scholar
  13. 13.
    Maddalena, L., Petrosino, A.: A self-organizing approach to background subtraction for visual surveillance applications. IEEE Trans. Image Process. 17(7), 1168–1177 (2008). doi: 10.1109/TIP.2008.924285 CrossRefMathSciNetGoogle Scholar
  14. 14.
    Munkelt, O., Kirchner, H.: STABIL: A system for monitoring persons in image sequences. In: Image and Video Processing IV, 1996. In: Proceedings of the Society of Photo-Optical Instrumentation Engineers (SPIE), vol. 2666, pp. 163–179 (1996)Google Scholar
  15. 15.
    Piccardi, M.: Background subtraction techniques: a review. In: IEEE International Conference on Systems, Man and Cybernetics 4, 3099–3104 (2004). doi: 10.1109/ICSMC.2004.1400815
  16. 16.
    Power, P., Schoonees, J.: Understanding background mixture models for foreground segmentation. In: Proceedings Image and Vision Computing New Zealand, pp. 267–271 (2002)Google Scholar
  17. 17.
    Regazzoni, C., Ramesh, V., Foresti, G.L.: Special issue on video communications, processing, and understanding for third generation surveillance systems. Proc. IEEE 89(10), 1355–1539 (2001)CrossRefGoogle Scholar
  18. 18.
    Sánchez Miralles, A., Sanz Bobi, M.A.: Real time dynamic ellipsoidal neural network (RTDENN). In: International Conference on Signal Processing, Robotics and Automation, pp. 1991–1995 (2002)Google Scholar
  19. 19.
    Sánchez Miralles, A., Sanz Bobi, M.A.: Global path planning in gaussian probabilistic maps. J. Intell. Robot. Syst. 40(1), 89–102 (2004). doi: 10.1023/B:JINT.0000034339.13257.e6
  20. 20.
    Miralles, Sánchez: A., Sanz Bobi, M.A.: A neural-based model for fast continuous and global robot location. J. Intell. Robot. Syst. 46(3), 221–243 (2006). doi: 10.1007/s10846-006-9046-4
  21. 21.
    Specht, D.F.: A general regression neural network. IEEE Trans. Neural Netw. 2(6), 568–576 (1991). doi: 10.1109/72.97934 CrossRefGoogle Scholar
  22. 22.
    Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. IEEE Comput. Soc. Conf. Comput. Vision Pattern Recogn. 2, 246–252 (1999)Google Scholar
  23. 23.
    Tsai, D.M., Lai, S.C.: Independent component analysis-based background subtraction for indoor surveillance. IEEE Trans. Image Process. 18(1), 158–167 (2009). doi: 10.1109/TIP.2008.2007558 CrossRefMathSciNetGoogle Scholar
  24. 24.
    Viola, P., Jones, M.J., Snow, D.: Detecting pedestrians using patterns of motion and appearance. Int. J. Comput. Vis. 63, 153–161 (2005). doi: 10.1007/s11263-005-6644-8 Google Scholar
  25. 25.
    Yu, T., Zhang, C., Cohen, M., Rui, Y., Wu, Y.: Monocular video foreground/background segmentation by tracking spatial-color gaussian mixture models. In: IEEE Workshop on Motion and Video Computing, WMVC ’07 (2007)Google Scholar
  26. 26.
    Yu, X., Chen, X., Zhang, H.: Accurate motion detection in dynamic scenes based on ego-motion estimation and optical flow segmentation combined method. In: 2011 Symposium on Photonics and Optoelectronics (SOPO), pp. 1–4 (2011) doi: 10.1109/SOPO.2011.5780637
  27. 27.
    Zhan, C., Duan, X., Xu, S., Song, Z., Luo, M.: An improved moving object detection algorithm based on frame difference and edge detection. In: Fourth International Conference on Image and Graphics, 2007. ICIG 2007, pp. 519–523 (2007). doi: 10.1109/ICIG.2007.153
  28. 28.
    Zhang, S., Yao, H., Liu, S.: Dynamic background subtraction based on local dependency histogram. Int. J. Pattern Recogn. Artif. Intell. 23(7), 1397–1419 (2009)CrossRefGoogle Scholar
  29. 29.
    Zivkovic, Z.: Improved adaptive gaussian mixture model for background subtraction. In: Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004, vol. 2, pp. 28–31 (2004). doi: 10.1109/ICPR.2004.1333992

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Manuel Alvar
    • 1
  • Andrea Rodriguez-Calvo
    • 1
  • Alvaro Sanchez-Miralles
    • 1
  • Alvaro Arranz
    • 1
  1. 1.Institute for Research in TechnologyComillas Pontifical UniversityMadridSpain

Personalised recommendations