Object Tracking Using Genetic Evolution Based Kernel Particle Filter

  • Qicong Wang
  • Jilin Liu
  • Zhigang Wu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4040)


A new particle filter, which combines genetic evolution and kernel density estimation, is proposed for moving object tracking. Particle filter (PF) solves non-linear and non-Gaussian state estimation problems in Monte Carlo simulation using importance sampling. Kernel particle filter (KPF) improves the performance of PF by using density estimation of broader kernel. However, it has the problem which is similar to the impoverishment phenomenon of PF. To deal with this problem, genetic evolution is introduced to form new filter. Genetic operators can ameliorate the diversity of particles. At the same time, genetic iteration drives particles toward their close local maximum of the posterior probability. Simulation results show the performance of the proposed approach is superior to that of PF and KPF.


Particle Filter Importance Sampling Object Tracking Kernel Density Estimation Posterior Density 
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.
    Nummiaro, K., Koller-Meier, E., Gool, L.V.: An Adaptive Color-Based Particle Filter. Image and Vision Computing 21, 99–110 (2003)CrossRefGoogle Scholar
  2. 2.
    Isard, M., Blake, A.: CONDENSATION–Conditional Density Propagation for Visual Tracking. International Journal on Computer Vision 29, 5–28 (1998)CrossRefGoogle Scholar
  3. 3.
  4. 4.
    Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T.: A Tutorial on Particle Filters for Online Nonlinear/Non-gaussian Bayesian Tracking. IEEE Transactions on Signal Processing 50, 174–188 (2002)CrossRefGoogle Scholar
  5. 5.
    MacCormick, J., Blake, A.: Partitioned Sampling, Articulated Objects and Interface-quality Hand Tracking. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 3–19. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  6. 6.
    MacCormick, J., Blake, A.: A Probabilistic Exclusion Principle for Tracking Multiple Objects. In: Proceedings of International Conference on Computer Vision, pp. 572–578 (1999)Google Scholar
  7. 7.
    Deutscher, J., Blake, A., Reid, I.: Articulated Body Motion Capture by Annealed Particle Filtering. In: Proceedings of Computer Vision and Pattern Recognition, pp. 126–133 (2000)Google Scholar
  8. 8.
    Hwang, S.W., Kim, E.Y., Park, S.H.: Object Extraction and Tracking Using Genetic Algorithms. In: Proceedings of International Conference on Image Processing, pp. 383–386 (2001)Google Scholar
  9. 9.
    Lehn-Schioler, T., Erdogmus, D., Principe, J.C.: Parzen Particle Filters. In: Proceedings of International Conference on Acoustics, Speech, and Signal Processing, pp. 781–784 (2004)Google Scholar
  10. 10.
    Chang, C., Ansari, R.: Kernel Particle Filter: Iterative Sampling for Efficient Visual Tracking. In: Proceedings of International Conference on Image Processing, pp. 977–980 (2003)Google Scholar
  11. 11.
    Doucet, A., Freitas, N., de Gordon, N.: Sequential Monte Carlo Methods in Practice. Springer, New York (2001)zbMATHGoogle Scholar
  12. 12.
    Hu, C.B., Yu, Q.F., Li, Y., Ma, S.D.: Extraction of Parametric Human Model for Posture Recognition Using Genetic Algorithm. In: Proceedings of International Conference on Automatic Face and Gesture Recognition (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Qicong Wang
    • 1
  • Jilin Liu
    • 1
  • Zhigang Wu
    • 2
  1. 1.Departmant of Information and Electronics EngineeringZhejiang UniversityHangzhouChina
  2. 2.College of Information & Electronics EngineeringTaizhou UniversityLinhaiChina

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