Particle Filter Based Object Tracking with Discriminative Feature Extraction and Fusion

  • Yao Shen
  • Parthasarathy Guturu
  • Thyagaraju Damarla
  • Bill P. Buckles
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5359)


This paper presents an object tracking algorithm based on the unscented particle filtering (UPF) approach. In this algorithm, occlusion tolerant features are first obtained for the images of the object in the consecutive frames based on the color, texture and shape (edge) information, and then a variant of the Fisher’s linear discriminant function approach is applied for reducing the dimensionality of the feature space. Similarities of the two images in each feature dimension are computed by matching the histograms of the quantized feature values, and finally these similarity values are aggregated into an over all similarity measure by a novel feature fusion technique embedded in the UPF framework. Results of experimentation with two different data sets indicate that our algorithm is both efficacious in handling severe occlusions (almost as high as 80%) and efficient with respect to tracking accuracy ...


Particle Filter Object Tracking Tracking Accuracy Proposal Distribution Feature Extraction Process 
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.


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  1. 1.
    Wang, J., Yagi, Y.: Integrating Color and Shape-Texture Features for Adaptive Real-Time Object Tracking. IEEE Trans. Image Process. 1, 235–240 (2008)CrossRefGoogle Scholar
  2. 2.
    Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. 1, 564–577 (2003)CrossRefGoogle Scholar
  3. 3.
    Merwe, R.v.d., Doucet, A., de Freitas, N., Wan, E.: The Unscented Particle Filter. In: Leen, T.K., Dietterich, T.G., Tresp, V. (eds.) Advances in Neural Information Processing Systems, NIPS13 (December 2000)Google Scholar
  4. 4.
    Arulampalam, S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for on-line non-linear/non-Gaussian Bayesian tracking. IEEE Trans. Signal Process 2, 174–188 (2002)CrossRefGoogle Scholar
  5. 5.
    Nummiaro, K., Koller-Meier, E., Van Gool, L.: A color-based particle filter. In: Proc.of Workshop on Generative-Model-Based Vision, pp. 53–60 (June 2002)Google Scholar
  6. 6.
    Vermaak, J., Perez, P., Gangnet, M., Blake, A.: Towards improved observation models for visual tracking: selective adaptation. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350. Springer, Heidelberg (2002)Google Scholar
  7. 7.
    Maggio, E., Smeraldi, F., Cavallaro, A.: Adaptive Multifeature Tracking in a Particle Filtering Framework. IEEE Trans. Circuits and Systems for Video Technology 10, 1348–1359 (2007)CrossRefGoogle Scholar
  8. 8.
    Petrou, M., Sevilla, P.G.: Image Processing: Dealing With Texture, 1st edn. Wiley, Chichester (2006)CrossRefGoogle Scholar
  9. 9.
    Bellman, R.: Adaptive Control Processes: A Guided Tour. Princeton University Press, Princeton (1961)CrossRefzbMATHGoogle Scholar
  10. 10.
    Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, New York (1990)zbMATHGoogle Scholar
  11. 11.
    Bhattacharyya, A.: On a measure of divergence between two statistical populations defined by probability distributions. Bull. Calcutta Math. Soc. 35, 99–109 (1943)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Shen, C., Hengel, A.V.D., Dick, A.: Probabilistic multiple cue integration for particle filter based tracking. In: Proc. 7th Int. Conf. Digital Image Comput., Sydney, Australia, pp. 309–408 (December 2003)Google Scholar
  13. 13.
    de la Torre, F., Gang, S., McKenna, S.: View-based adaptive affine tracking. In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 828–842. Springer, Heidelberg (1998)Google Scholar
  14. 14.
    Black, M.J., Jepson, A.D.: Eigentracking: Robust matching and tracking of articulated objects using a view-based representation. In: Buxton, B.F., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1064, pp. 329–342. Springer, Heidelberg (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Yao Shen
    • 1
  • Parthasarathy Guturu
    • 1
  • Thyagaraju Damarla
    • 2
  • Bill P. Buckles
    • 1
  1. 1.University of North TexasDentonUSA
  2. 2.Army Research LabAdelphiUSA

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