Advertisement

Video Target Tracking Based on a New Adaptive Particle Swarm Optimization Particle Filter

  • Feng Liu
  • Shi-bin Xuan
  • Xiang-pin Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7996)

Abstract

To improve accuracy and robustness of video target tracking, a tracking algorithm based on a new adaptive particle swarm optimization particle filter (NAPSOPF) is proposed. A novel inertia weight generating strategy is proposed to balance adaptively the global and local searching ability of the algorithm. This strategy can adjust the particle search range to adapt to different motion levels. The possible position of moving target in the first frame image is predicted by particle filter. Then the proposed NAPSO is utilized to search the smallest Bhattacharyya distance which is most similar to the target template. As a result, the algorithm can reduce the search for matching and improve real-time performance. Experimental results show that the proposed algorithm has a good tracking accuracy and real-time in case of occlusions and fast moving target in video target tracking.

Keywords

Adaptive Particle Swarm Optimization Particle Filter Video Target Tracking Occlusions Fast Moving Target Bhattacharyya Distance 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Collns, R., Lipton, A., Kanadeandt.: A System for Video Surveillance and Monitoring VSAM Final report. Carnegie Mellon University (2000) Google Scholar
  2. 2.
    Belagiannis, V., Schubert, F., Navab, N., Ilic, S.: Segmentation based particle filtering for real-time 2D object tracking. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part IV. LNCS, vol. 7575, pp. 842–855. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    Loris, B., Marco, C., Vittorio, M.: Decentralized Particle Filters for Joint Individual-Group Tracking. In: CVPR (2012)Google Scholar
  4. 4.
    Chong, Y., Chen, R., Li, Q., Zheng, C.-H.: Particle filter based on multiple cues fusion for pedestrian tracking. In: Huang, D.-S., Gupta, P., Zhang, X., Premaratne, P. (eds.) ICIC 2012. CCIS, vol. 304, pp. 321–327. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  5. 5.
    Wang, A.X., Li, J.J.: Target Tracking Based on Multi-core Particle Filtering. Computer Science 39(8), 296–299 (2012)Google Scholar
  6. 6.
    Doucet, A., Godsill, S.: On Sequential Monte Carlo Sampling Methods for Bayesian Filtering. Statistics and Computing 10(1), 197–208 (2000)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Katja, N., Esther, K., Luc, V.G.: Object Tracking with and Adaptive Color-based Particle filter. In: Proceedings of the 24th DAGM Symposium on Pattern Recognition, Zurich, Switzerland, September 16-18, pp. 353–360 (2002)Google Scholar
  8. 8.
    Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proc of the IEEE Intl. Conf. on Neural Networks, Perth, Australia, pp. 1942–1948. IEEE Service Center, Piscataway (1995)CrossRefGoogle Scholar
  9. 9.
    Shi, Y.H., Eberhart, R.C.: A Modified Particle Swarm Optimizer. In: Proceedings of The IEEE Congress on Evolutionary Computation, pp. 69–73. IEEE Service Center, Piscataway (1998)Google Scholar
  10. 10.
    Li, A.P.: Research on Tracking Algorithm for Visual Target under Complex Environments, pp. 20–31. Shanghai Jiao Tong University, Shanghai (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Feng Liu
    • 1
  • Shi-bin Xuan
    • 1
    • 2
  • Xiang-pin Liu
    • 1
  1. 1.College of Information Science and EngineeringGuangxi University for NationalitiesNanningChina
  2. 2.Guangxi Key Laboratory of Hybrid Computation and IC Design AnalysisNanningChina

Personalised recommendations