Active Contour Tracking of Moving Objects Using Edge Flows and Ant Colony Optimization in Video Sequences

  • Dong-Xian Lai
  • Yuan-Hsiang Chang
  • Zhi-He Zhong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5414)

Abstract

Object segmentation and tracking are important techniques in video applications. In this paper, we present a novel system for active contour tracking of moving objects in video sequences. Our method includes preprocessing to identify an initial object contour, and object contour segmentation to refine the contour of the moving object. The edge flows and ant colony optimization are incorporated to improve the efficiency during system convergence. Experimental results demonstrated that our system has achieved the automatic segmentation accuracy of < 1 pixel on average as compared with manual segmentation results. In summary, our system is particularly useful in segmenting and tracking a moving object without constructing a background model for a video scene. Ultimately, our system could be used in object-based video coding or other analysis such as behavior analysis in video surveillance systems.

Keywords

Active contour model Ant colony optimization Edge flow Object tracking 

References

  1. 1.
    Xiang, T., Gong, S.: Video behavior profiling for anomaly detection. IEEE Trans. Pattern Anal. Mach. Intell. 30, 893–908 (2008)CrossRefGoogle Scholar
  2. 2.
    Gupta, A., Mittal, A., Davis, L.S.: Constraint integration for efficient multiview pose estimation with self-occlusions. IEEE Trans. Pattern Anal. Mach. Intell. 30, 493–506 (2008)CrossRefGoogle Scholar
  3. 3.
    Sundaramoorthi, G., Yezzi, A., Mennucci, A.C.: Coarse-to-fine segmentation and tracking using sobolev active contours. IEEE Trans. Pattern Anal. Mach. Intell. 30, 851–864 (2008)CrossRefMATHGoogle Scholar
  4. 4.
    Zhao, T., Nevatia, R., Wu, B.: Segmentation and tracking of multiple humans in crowded environments. IEEE Trans. Pattern Anal. Mach. Intell. 30, 1198–1211 (2008)CrossRefGoogle Scholar
  5. 5.
    Han, B., Comaniciu, D., Zhu, Y., Davis, L.S.: Sequential kernel density approximation and its application to real-time visual tracking. IEEE Trans. Pattern Anal. Mach. Intell. 30, 1186–1197 (2008)CrossRefGoogle Scholar
  6. 6.
    Briassouli, A., Ahuja, N.: Extraction and Analysis of multiple periodic motions in video sequence. IEEE Trans. Pattern Anal. Mach. Intell. 29, 1244–1261 (2007)CrossRefGoogle Scholar
  7. 7.
    Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24, 603–619 (2002)CrossRefGoogle Scholar
  8. 8.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22, 888–905 (2000)CrossRefGoogle Scholar
  9. 9.
    Xu, N., Ahuja, N.: Object contour tracking using graph cuts based active contours. In: IEEE Proceedings International Conference on Image Processing, vol. 3, pp. III-277–III-280 (2002) Google Scholar
  10. 10.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision 1, 321–331 (1988)CrossRefMATHGoogle Scholar
  11. 11.
    Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, p. 252 (1999)Google Scholar
  12. 12.
    Elgammal, A., Duraiswami, R., Hardwood, D., Davis, L.S.: Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. IEEE Proceeding 90(7), 1151–1163 (2002)CrossRefGoogle Scholar
  13. 13.
    Canny edge detection tutorial (2008), http://www.pages.drexel.edu/~weg22/can_tut.html
  14. 14.
    Gonzalez, R.C., Wood, R.E.: Digital Image Processing, 2nd edn. Prentice Hall, New Jersey (2002)Google Scholar
  15. 15.
    Dorigo, M., Maniezzo, V., Colorni, A.: The ant system: Optimazation by a colony of cooperating agents. IEEE Trans. Systems, Man, and Cybernetics 26, 29–41 (1996)CrossRefGoogle Scholar
  16. 16.
    Wang, X.N., Feng, Y.J., Feng, Z.R.: Ant colony optimization for image segmentation. In: IEEE Proceeding International conference on Machine Learning and Cybernetics, vol. 9, pp. 5355–5360 (2005)Google Scholar
  17. 17.
    Ma, W.Y., Manjunath, B.S.: Edge flow: A framework of boundary detection and image segmentation. In: IEEE Proceedings. Computer Society Conference on Computer Vision and Pattern Recognition, pp. 744–749 (1997)Google Scholar
  18. 18.
    Litvin, A., Konrad, J., Karl, W.C.: Probabilistic video stabilization using kalman filtering and mosaicking. In: Proceedings of SPIE-IS&T Electronic Imaging, SPIE, vol. 5002, pp. 663–674 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Dong-Xian Lai
    • 1
  • Yuan-Hsiang Chang
    • 1
  • Zhi-He Zhong
    • 1
  1. 1.Dept.of Information & Computer EngineeringChung Yuan Christian Univ.JhingliTaiwan, R.O.C.

Personalised recommendations