An Attempt to Segment Foreground in Dynamic Scenes

  • Xiang Xiang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6938)


In general, human behavior analysis relies on a sequence of human segments, e.g. gait recognition aims to address human identification based on people’s manners of walking, and thus relies on the segmented silhouettes. Background subtraction is the most widely used approach to segment foreground, while dynamic scenes make it difficult to work. In this paper, we propose to combine Mean-Shift-based tracking with adaptive scale and Graph-cuts-based segmentation with label propagation. The average precision on a number of sequences is 0.82, and the average recall is 0.72. Besides, our method only requires weak user interaction and is computationally efficient. We compare our method with its variant without label propagation, as well as GrabCut. For the tracking module only, we compare Mean Shift with several state-of-the-art methods (i.e. OnlineBoost, SemiBoost, MILTrack, FragTrack).


Label Propagation Dynamic Scene Shift Vector Visual Hull Multiple Instance Learn 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Lee, L., Grimson, W.E.L.: Gait Analysis for Recognition and Classification. In: Proc. IEEE Int. Conf. Automatic Face and Gesture Recognition, pp. 148–155 (2002)Google Scholar
  2. 2.
    Boykov, Y., Kolmogorov, V.: An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision. IEEE Trans. PAMI 26(9) (2004)Google Scholar
  3. 3.
    Malcolm, J., Rathi, Y., Tannenbaum, A.: Multi-Object Tracking Through Clutter Using Graph Cuts. In: Proc. IEEE ICCV, pp. 1–5 (2007)Google Scholar
  4. 4.
    Piccardi, M.: Background Subtraction Techniques: A Review. In: Proc. IEEE Int. Conf. Systems, Man and Cybernetics, vol. 4, pp. 3099–3104 (2004)Google Scholar
  5. 5.
    Sun, J., Zhang, W., Tang, X., Shum, H.Y.: Background Cut. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part II. LNCS, vol. 3952, pp. 628–641. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Bray, M., Kohli, P., Torr, P.: Posecut: Simultaneous Segmentation and 3D Pose Estimation of Humans using Dynamic Graph-Cuts. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part II. LNCS, vol. 3952, pp. 642–655. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Criminisi, A., Cross, G., Blake, A., Kolmogorov, V.: Bilayer Segmentation of Live Video. In: Proc. IEEE CVPR, pp. 53–60 (2006)Google Scholar
  8. 8.
    Juan, O., Boykov, Y.: Active Graph Cuts. In: Proc. IEEE CVPR, vol. 1, pp. 1023–1029 (2006)Google Scholar
  9. 9.
    Li, Y., Sun, J., Shum, H.Y.: Video Object Cut and Paste. Proc. ACM SIGGRAPH 2005, ACM Trans. Graphics 24(3), 595–600 (2005)Google Scholar
  10. 10.
    Zhong, F., Qin, X., Peng, Q.: Transductive Segmentation of Live Video with Non-Stationary Background. In: Proc. IEEE CVPR, pp. 2189–2196 (2010)Google Scholar
  11. 11.
    Niebles, J., Han, B., Ferencz, A., Fei-Fei, L.: Extracting Moving People from Internet Videos. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 527–540. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  12. 12.
    Niebles, J., Han, B., Fei-Fei, L.: Efficient Extraction of Human Motion Volumes by Tracking. In: Proc. IEEE CVPR, pp. 655–662 (2010)Google Scholar
  13. 13.
    Grundmann, M., Kwatra, V., Han, M., Essa, I.: Efficient Hierarchical Graph-Based Video Segmentation. In: Proc. IEEE CVPR, pp. 2141–2148 (2010)Google Scholar
  14. 14.
    Bai, X., Wang, J., Sapiro, G.: Dynamic Color Flow: A Motion-Adaptive Color Model for Object Segmentation in Video. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 617–630. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  15. 15.
    Bugeau, A., Perez, P.: Detection and Segmentation of Moving Objects in Highly Dynamic Scenes. In: Proc. IEEE CVPR (2007)Google Scholar
  16. 16.
    Ren, X., Malik, J.: Tracking as Repeated Figure/Ground Segmentation. In: CVPR (2007)Google Scholar
  17. 17.
    Grabner, H., Bischof, H.: On-line Boosting and Vision. In: Proc. CVPR, pp. 260–267 (2006)Google Scholar
  18. 18.
    Grabner, H., Leistner, C., Bischof, H.: Semi-supervised On-Line Boosting for Robust Tracking. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 234–247. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  19. 19.
    Babenko, B., Yang, M.H., Belongie, S.: Visual Tracking with Online Multiple Instance Learning. In: Proc. IEEE CVPR, pp. 983–990 (2009)Google Scholar
  20. 20.
    Comaniciu, D., Ramesh, V., Meer, T.: Real-Time Tracking of Non-Rigid Objects Using Mean Shift. In: Proc. IEEE CVPR, vol. 2, pp. 142–149 (2000)Google Scholar
  21. 21.
    Boykov, Y., Jolly, M.: Interactive Graph Cuts for Optimal Boundary and Region Segmentation of Objects in N-D Images. In: Proc. IEEE ICCV, vol. 1, pp. 105–112 (2001) Google Scholar
  22. 22.
    Rother, C., Kolmogorov, V., Blake, A.: “GrabCut”: Interactive Foreground Extraction using Iterated Graph Cuts. Proc. ACM SIGGRAPH 2004, ToG 23(3), 309–314 (2004)CrossRefGoogle Scholar
  23. 23.
    Adam, A., Rivlin, E., Shimshoni, I.: Robust Fragments-based Tracking using the Integral Histogram. In: Proc. IEEE CVPR, vol. 1, pp. 798–805 (2006)Google Scholar
  24. 24.
    Badrinarayanan, V., Galasso, F., Cipolla, R.: Label Propagation in Video Sequences. In: Proc. IEEE CVPR, pp. 3265–3272 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Xiang Xiang
    • 1
  1. 1.Key Lab of Intelligent Information Processing of CAS, Institute of Computing TechnologyChinese Academy of Sciences (CAS)BeijingChina

Personalised recommendations