OSA's Digital Library

Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: Joseph N. Mait
  • Vol. 49, Iss. 31 — Nov. 1, 2010
  • pp: 6043–6056

Color spatial feature-based approach for multiple-vehicle tracking

Qi Wei, Zhang Xiong, and Chao Li  »View Author Affiliations


Applied Optics, Vol. 49, Issue 31, pp. 6043-6056 (2010)
http://dx.doi.org/10.1364/AO.49.006043


View Full Text Article

Enhanced HTML    Acrobat PDF (2909 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

We present a robust approach to multiple-vehicle tracking, which combines deterministic and probabilistic methods. The observation model is built with an improved color correlogram, which is a feature vector with a compact correlogram using overlapping fragmentation to make the ideal form to measure similarity with a Bhattacharyya coefficient. The observation and state model of multiple vehicles is given under a CamShift framework. To overcome the disadvantage of particle impoverishment, a layered particle filter architecture embedding Camshift is proposed, which considers both the concentration and the diversity of the particles, and the particle set can better represent the posterior probability density. We also present experiments using a real video sequence to verify the proposed method.

© 2010 Optical Society of America

OCIS Codes
(100.5010) Image processing : Pattern recognition
(100.4993) Image processing : Pattern recognition, Baysian processors
(100.4999) Image processing : Pattern recognition, target tracking

ToC Category:
Image Processing

History
Original Manuscript: June 1, 2010
Revised Manuscript: September 15, 2010
Manuscript Accepted: September 23, 2010
Published: October 26, 2010

Citation
Qi Wei, Zhang Xiong, and Chao Li, "Color spatial feature-based approach for multiple-vehicle tracking," Appl. Opt. 49, 6043-6056 (2010)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-49-31-6043


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. D. Comaniciu, V. Ramesh, and P. Meer, “Kernel-based object tracking,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 564–577 (2003). [CrossRef]
  2. P. Pérez, C. Hue, J. Vermaak, and M. Gangnet, “Color-based probabilistic tracking,” Lect. Notes Comput. Sci. 2350, 661–675 (2002).
  3. K. Nummiaro, E. Koller-Meier, and L. Van Gool, “An adaptive color-based particle filter,” Image Vision Comput. 21, 99–110(2003). [CrossRef]
  4. Z. W. Wang, X. K. Yang, Y. Xu, and S. Y. Yu, “Camshift guided particle filter for visual tracking,” in 2007 IEEE Workshop on Signal Processing Systems (IEEE, 2007), pp. 301–306. [CrossRef]
  5. S. T. Birchfield and S. Rangarajan, “Spatiograms versus histograms for region-based tracking,” in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2005), Vol. 2, pp. 1158–1163.
  6. S. T. Birchfield and S. Rangarajan, “Spatial histograms for region-based tracking,” ETRI J. 29, 697–699 (2007). [CrossRef]
  7. P. Kumar, A. Dick, and M. J. Brooks, “Multiple target tracking with an efficient compact color correlogram,” in 10th International Conference on Control, Automation, Robotics and Vision (IEEE, 2008), pp. 699–704. [CrossRef]
  8. J. Garcia, J. Campos, and C. Ferreira, “Circular-harmonic minimum average correlation energy filter for color pattern recognition,” Appl. Opt. 33, 2180–2187 (1994). [CrossRef] [PubMed]
  9. O. Gualdrón, J. Nicolás, J. Campos, and M. Yzuel, “Rotation invariant color pattern recognition by use of a three-dimensional Fourier transform,” Appl. Opt. 42, 1434–1440(2003). [CrossRef] [PubMed]
  10. P. García-Martínez, J. Otón, J. Vallés, and H. Arsenault, “Nonlinear pattern recognition correlators based on color-encoding single-channel systems,” Appl. Opt. 43, 425–432 (2004). [CrossRef] [PubMed]
  11. D. Comaniciu, V. Ramesh, and P. Meer, “Real-time tracking of non-rigid objects using mean shift,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2000), pp. 142–149. [CrossRef]
  12. N. S. Peng, J. Yang, Z. Liu, and F. C. Zhang, “Automatic selection of kernel-bandwidth for mean-shift object tracking,” J. Software 16, 1542–1551 (2005) (in Chinese). [CrossRef]
  13. Q. Zhao and H. Tao, “Object tracking using color correlogram,” in 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (IEEE, 2005), pp. 263–270. [CrossRef] [PubMed]
  14. Q. Zhao and H. Tao, “Motion observability analysis of the simplified color correlogram for visual tracking,” in Proceedings of the 8th Asian conference on Computer Vision (Springer-Verlag, 2007), Vol. I, pp. 345–354.
  15. H. Sheng, Z. Xiong, J. N. Weng, and Q. Wei, “An approach to detecting abnormal vehicle events in complex factors over highway surveillance video,” Science in China Ser. E 51, 199–208 (2008). [CrossRef]
  16. H. Sheng, C. Li, Q. Wei, and Z. Xiong, “Real-time detection of abnormal vehicle events with multi-feature over highway surveillance video,” in 11th International IEEE Conference on Intelligent Transportation Systems (IEEE, 2008), pp. 550–556.
  17. T. Ojala, M. Rautiainen, E. Matinmikko, and M. Aittola, “Semantic image retrieval with HSV correlograms,” in Proceedings of the 12th Scandinavian Conference on Image Analysis (Danish Society of Pattern Recognition and Image Analysis, 2001), pp. 621–627.
  18. G. R. Bradski, “Computer vision face tracking as a component of a perceptual user interface,” in Proceedings of the IEEE Workshop on Applications of Computer Vision (IEEE, 1998), pp. 214–219.
  19. S. Hu, G. Liang, and Z. Jing, “Robust object tracking algorithm in natural environments,” in Proceedings of the 2nd International Conference on Natural Computation (Springer, 2006), pp. 516–525.
  20. B. Zhang, W. F. Tian, and Z. H. Jin, “Robust appearance-guided particle filter for object tracking with occlusion analysis,” Int. J. Electron. Commun. 62, 24–32 (2008). [CrossRef]

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