Probability Evolutionary Algorithm Based Human Body Tracking

  • Shuhan Shen
  • Weirong Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3907)


A novel evolutionary algorithm called Probability Evolutionary Algorithm (PEA), and a method based on PEA for visual tracking of human body are presented. PEA is inspired by the Quantum computation and the Quantum-inspired Evolutionary Algorithm, and it has a good balance between exploration and exploitation with very fast computation speed. In the PEA based human tracking framework, tracking is considered to be a function optimization problem, so the aim is to optimize the matching function between the model and the image observation. Then PEA is used to optimize the matching function. Experiments on synthetic and real image sequences of human motion demonstrate the effectiveness, significance and computation efficiency of the proposed human tracking method.


Human Motion Human Model Visual Tracking Tracking Result Matching Function 
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.
    Hu, W.M., Tan, T.N., Wang, L., Maybank, S.J.: A survey on visual surveillance of object motion and behaviors. IEEE Trans. on System Man and Cybernetics 34, 334–351 (2004)Google Scholar
  2. 2.
    Gavrila, D., Davis, L.: 3D model based tracking of humans inaction: A multiview approach. In: IEEE Proceedings of International Conference on Computer Vision and Pattern Recognition, San Francisco, California, pp. 73–80 (1996)Google Scholar
  3. 3.
    Isard, M., Blake, A.: CONDENSATION-conditional density propagation for visual tracking. International Journal of Computer Vision 29, 5–28 (1998)CrossRefGoogle Scholar
  4. 4.
    Deutscher, J., Davidson, A., Reid, I.: Articulated partitioning of high dimensional search spaces associated with articulated body motion capture. In: IEEE Proceedings of International Conference on Computer Vision and Pattern Recognition, Hawaii, pp. 669–676 (2001)Google Scholar
  5. 5.
    Wu, Y., Hua, G., Yu, T.: Tracking Articulated Body by Dynamic Markov Network. In: Proceedings of the Ninth IEEE International Conference on Computer Vision, pp. 1096–1101 (2003)Google Scholar
  6. 6.
    Zhao, T., Nevatia, R.: Tracking Multiple Humans in Crowded Environment. In: IEEE Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 342–349 (2004)Google Scholar
  7. 7.
    Han, K.H., Kim, J.H.: Quantum-Inspired Evolutionary Algorithm for a Class of Combinatorial Optimization. IEEE Trans. on Evolutionary Computing 6, 580–593 (2002)CrossRefGoogle Scholar
  8. 8.
    Hey, T.: Quantum computing: An introduction. Computing & Control Engineering Journal 10, 105–121 (1996)CrossRefGoogle Scholar
  9. 9.
    Shen, S.H., Jiang, W.K., Chen, W.R.: Research of Probability Evolutionary Algorithm. In: 8th International Conference for Young Computer Scientists, Beijing, pp. 93–97 (2005)Google Scholar
  10. 10.
    Poser Software: Available from

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shuhan Shen
    • 1
  • Weirong Chen
    • 1
  1. 1.School of Electrical EngineeringSouthwest Jiaotong UniversityChengduChina

Personalised recommendations