OSA's Digital Library

Journal of the Optical Society of America A

Journal of the Optical Society of America A

| OPTICS, IMAGE SCIENCE, AND VISION

  • Editor: Stephen A. Burns
  • Vol. 26, Iss. 2 — Feb. 1, 2009
  • pp: 342–349

Moving object detection based on shape prediction

Xiang Zhang and Jie Yang  »View Author Affiliations


JOSA A, Vol. 26, Issue 2, pp. 342-349 (2009)
http://dx.doi.org/10.1364/JOSAA.26.000342


View Full Text Article

Enhanced HTML    Acrobat PDF (589 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

Statistical analysis is widely used for moving object detection from video sequences, and generally it assumes that the recent segmentations are the desirable samples. However, we think the recent segmentations are not desirable for the detection of deformable objects, such as a pedestrian. We present an object detection framework that is designed to select the most desirable samples from all historical segmentations for statistical analysis. Central to this algorithm is that the shape evolution of the deformable object is learned from historical segmentations by an autoregressive model. Based on the learned model, the shape of the moving object in a current frame is predicted. Those historical segmentations that show similar shape to the predicted shape are selected for statistical analysis instead of the recent segmentations. Definitive experiments demonstrate the performance of the proposed method.

© 2009 Optical Society of America

OCIS Codes
(100.2960) Image processing : Image analysis
(200.3050) Optics in computing : Information processing
(330.1880) Vision, color, and visual optics : Detection
(150.1135) Machine vision : Algorithms

ToC Category:
Image Processing

History
Original Manuscript: July 15, 2008
Revised Manuscript: October 25, 2008
Manuscript Accepted: November 22, 2008
Published: January 28, 2009

Citation
Xiang Zhang and Jie Yang, "Moving object detection based on shape prediction," J. Opt. Soc. Am. A 26, 342-349 (2009)
http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-26-2-342


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. E. Goujou, J. Miteran, O. Laligant, F. Truchetet, and P. Gorria, “Human detection with a video surveillance system,” IEEE International Conference on Industrial Electronics, Control, and Instrumentation (IEEE, 1995), pp. 1179-1184.
  2. B. Leibe, K. Schindler, N. Cornelis, and L. V. Gool, “Coupled object detection and tracking from static cameras and moving vehicles,” IEEE Trans. Pattern Anal. Mach. Intell. 30, 1683-1698 (2008). [CrossRef] [PubMed]
  3. M. Andriluka, S. Roth, and B. Schiele, “People-tracking-by-detection and people-detection-by-tracking,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2008).
  4. B. Leibe, A. Leonardis, and B. Schiele, “Robust object detection with interleaved categorization and segmentation,” Int. J. Comput. Vis. 77, 259-289 (2008). [CrossRef]
  5. P. F. Felzenszwalb and D. P. Huttenlocher, “Pictorial structures for object recognition,” Int. J. Comput. Vis. 61, 55-79 (2005). [CrossRef]
  6. S. Osher and J. A. Sethian, “Fronts propagation with curvature dependent speed: algorithms based on Hamilton-Jacobi formulations,” J. Comput. Phys. 79, 12-49 (1988). [CrossRef]
  7. D. Cremers, “Dynamical statistical shape priors for level set based tracking,” IEEE Trans. Pattern Anal. Mach. Intell. 28, 1262-1273 (2006). [CrossRef] [PubMed]
  8. I. B. Ayed, S. Li, and I. Ross, “Tracking distributions with an overlap prior,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2008).
  9. I. Haritaoglu, D. Harwood, and L. S. Davis, “W4: Real-time surveillance of people and their activities,” IEEE Trans. Pattern Anal. Mach. Intell. 22, 809-830 (2000). [CrossRef]
  10. J. Zhong and S. Sclaroff, “Segmenting foreground objects from a dynamic textured background via a robust kalman filter,” IEEE International Conference on Computer Vision (IEEE, 2003), pp. 44-50. [CrossRef]
  11. A. Monnet, A. Mittal, N. Paragios, and V. Ramesh, “Background modeling and subtraction of dynamic scenes,” IEEE International Conference on Computer Vision (IEEE, 2003), pp. 1305-1312. [CrossRef]
  12. M. Harville, G. Gordon, and J. Woodfill, “Foreground segmentation using adaptive mixture models in color and depth,” in IEEE Workshop on Detection and Recognition of Events in Video (IEEE, 2001), pp. 3-11. [CrossRef]
  13. M. Heikkila and M. Pietikainen, “A texture-based method for modeling the background and detecting moving objects,” IEEE Trans. Pattern Anal. Mach. Intell. 28, 657-662 (2006). [CrossRef]
  14. M. Piccardi, “Background subtraction techniques: a review,” IEEE International Conference on Systems, Man and Cybernetics (IEEE, 2004), pp. 3099-3104.
  15. Y. Sheikh and M. Shah, “Bayesian modeling of dynamic scenes for object detection,” IEEE Trans. Pattern Anal. Mach. Intell. 27, 1778-1792 (2005). [CrossRef] [PubMed]
  16. A. Criminisi, G. Cross, A. Blake, and V. Kolmogorov, “Bilayer segmentation of live video,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2006), pp. 53-60.
  17. C. R. Wren, A. Azarbayejani, T. Darrell, and A. P. Pentland, “Pfinder: Real-time tracking of the human body,” IEEE Trans. Pattern Anal. Mach. Intell. 19, 780-785 (1997). [CrossRef]
  18. C. Stauffer and W. E. L. Grimson, “Learning patterns of activity using real-time tracking,” IEEE Trans. Pattern Anal. Mach. Intell. 22, 747-757 (2000). [CrossRef]
  19. L. Lu and G. D. Hager, “A nonparametric treatment for location/segmentation based visual tracking,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007).
  20. A. Elgammal, R. Duraiswami, D. Harwood, and L. S. Davis, “Background and foreground modeling using non-parametric kernel density estimation for visual surveillance,” Proc. IEEE 90, 1151-1163 (2002). [CrossRef]
  21. S. Mahamud, “Comparing belief propagation and graph cuts for novelty detection,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2006), pp. 1154-1159.
  22. A. Elgammal, R. Duraiswami, and L. S. Davis, “Efficient kernel density estimation using the fast Gauss transform with applications to color modeling and tracking,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 1499-1504 (2003). [CrossRef]
  23. C. Yang, R. Duraiswami, N. A. Gumerov, and L. S. Davis, “Improved fast Gauss transform and efficient kernel density estimation,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2003), pp. 664-671.
  24. J. Kato, T. Watanabe, S. Joga, J. Rittscher, and A. Blake, “An HMM-based segmentation method for traffic monitoring movies,” IEEE Trans. Pattern Anal. Mach. Intell. 24, 1291-1296 (2002). [CrossRef]
  25. K. A. Patwardhan, G. Sapiro, and V. Morellas, “Robust foreground detection in video using pixel layers,” IEEE Trans. Pattern Anal. Mach. Intell. 30, 746-751 (2008). [CrossRef] [PubMed]
  26. J. Sun, W. Zhang, X. Tang, and H. Y. Shum, “Background cut,” in Proceedings of European Conference on Computer Vision (ECCV, 2006), pp. 628-641.
  27. D. S. Zhang and G. Lu, “Review of shape representation and description techniques,” Pattern Recogn. 37, 1-19 (2004). [CrossRef]
  28. D. S. Zhang and G. Lu, “Generic Fourier descriptor for shape-based image retrieval,” in Proceedings of IEEE International Conference on Multimedia and Expo (ICME, 2000), pp. 425-428.
  29. G. Schwarz, “Estimating the dimension of a model,” Ann. Stat. 6, 461-464 (1978). [CrossRef]
  30. S. Haykin, Adaptive Filter Theory (Prentice Hall, 2001).
  31. V. Kolmogorov and R. Zabih, “What energy functions can be minimized via graph cuts,” IEEE Trans. Pattern Anal. Mach. Intell. 26, 147-159 (2004). [CrossRef] [PubMed]
  32. Y. Boykov, O. Veksler, and R. Zabih, “Fast approximate energy minimization via graph cuts,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1222-1239 (2001). [CrossRef]
  33. D. Greig, B. Porteous, and A. Seheult, “Exact maximum a posteriori estimation for binary images,” J. R. Stat. Soc. Ser. B (Methodol.) 51, 271-279 (1989).

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited