Hybrid Iterated Kalman Particle Filter for Object Tracking Problems

Amr M. Nagy, Ali Ahmed, Hala H. Zayed


Particle Filters (PFs), are widely used where the system is non Linear and non Gaussian. Choosing the importance proposal distribution is a key issue for solving nonlinear filtering problems. Practical object tracking problems encourage researchers to design better candidate for proposal distribution in order to gain better performance. In this correspondence, a new algorithm referred to as the hybrid iterated Kalman particle filter (HIKPF) is proposed. The proposed algorithm is developed from unscented Kalman filter (UKF) and iterated extended Kalman filter (IEKF) to generate the proposal distribution, which lead to an efficient use of the latest observations and generates more close approximation of the posterior probability density. Comparing with previously suggested methods(e.g PF, PF-EKF, PF-UKF, PF-IEKF), our proposed method shows a better performance and tracking accuracy. The correctness as well as validity of the algorithm is demonstrated through numerical simulation and experiment results.


  1. Arulampalam, M., Maskell, S., Gordon, N., and Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. In IEEE Transactions on Signal Processing. IEEE Signal Processing Society,Vol. 50, pp. 174-188.
  2. Broida, T. and Chellappa, R. (1986). Estimation of object motion parameters from noisy images. In IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE Computer Society Washington, DC, USA,Vol. 8, No. 1, pp. 90-99.
  3. Collins, R., Lipton, A., Kanade, T., Fujiyoshi, H., Duggins, D., Tsin, Y., Tolliver, D., Enomoto, N., and Hasegawa, O. (2000). System for video surveillance and monitoring. In Technical Report CMU-RI-TR-00-12. Robitics institute.
  4. Fen, X. and Ming., G. (2010). Pedestrian tracking using particle filter algorithm. In International Conference on Electrical and Control Engineering. IEEE Computer Society Washington, DC, USA,pp.1478-1481.
  5. Gelb, A. (1974). Applied Optimal Estimation. M.I.T. Press, Cambridge, 1st edition.
  6. Gordon, N., Salmond, D., and Smith, A. (1993). Novel approach to nonlinear/non-gaussian bayesian state estimation. In IEEF Proceedings Radar and Signal Processing. IET,Vol. 1, pp. 107-113.
  7. Isard, M. and Blake, A. (1998). Condensation conditional density propagation for visual tracking. In International Journal of Computer Vision. Kluwer Academic Publishers Hingham,MA,USA, Vol. 29, No. 1, pp. 5- 28.
  8. Julier, S. (2002). The scaled unscented transformation. In Proceedings of the 2002 American Control Conference2. IEEE Conference Publications,Vol 6,PP.4555- 4559.
  9. Julier, S., Jeffrey, J., and Uhlmann, K. (2002). Reduced sigma point filters for the propagation of means and covariances through nonlinear transformations. In In Proceedings of the American Control Conference. IEEE Conference Publications Vol 2, pp. 887-892.
  10. Julier, S. J. and Uhlmann, J. K. (2004). Unscented filtering and nonlinear estimation. In Proceedings of the IEEE. IEEE,Vol 92, pp. 401-422.
  11. kyung-min Cho, jeong-hun Jang, and ki-sang Hong (2001). Adaptive skin-color filter. In Pattern Recognition. Elsevier, Vol 34,pp. 1067-1073.
  12. Lefebvre, T. and Bruyninckx, H. (2004). Kalman filters for nonlinear systems: A comparison of performance. In International Journal of Control. Vol 77,pp.639-653.
  13. Liang-qun, L., Hong-bing, J., and Jun-hui, L. (2005). The iterated extended kalman particle filter. In Proceedings International Symposium on Communication and Information Technologies 2005. IEEE Conference Publications,Vol 2,pp.1213-1216.
  14. Lipton, A., Fujiyoshi, H., and Patil, R. (1998). Moving target classification and tracking from real-time video. In Proceeding of IEEE Workshop Applications of Computer Vision. IEEE Conference Publicationsn,pp. 8- 14.
  15. LIU, Y., Haizho, A., and Guangyou, X. (2001). Moving object detection and tracking based on background subtraction. In Proceeding of Society of Photo-Optical Instrument Engineers. Vol. 4554, pp. 62-66.
  16. McIvor, A. M. (2000). Background subtraction techniques. In Proceeding of Image and Vision Computing. IVCNZ00, Hamilton, New Zealand.
  17. Meyer, D., Denzler, J., and Niemann, H. (1998). Model based extraction of articulated objects in image sequences for gait analysis. In Proceeding of IEEE Int. Conf. Image Proccessing. IEEE Conference Publications,Vol 3,pp.78-81.
  18. Phung, S., Chai, D., and Bouzerdoum, A. (2003). Adaptive skin segmentation in color images. In Proceeding of IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE Conference Publications,Vol. 3, pp. 353-356.
  19. R Van der Merwe, A. D. (2000). The unscented particle filter. In Advances in Neural Information Processing Systems.
  20. Rawlings, J. and Bakshi, B. (2006). Particle filtering and moving horizon estimation. In Computers and Chemical Engineering. Elsevier,Vol 30,pp. 1529-1541.
  21. Ristic, B., Arulampalam, S., and Gordon, N. (2004). Beyond the Kalman filter: Particle filters for tracking applications. Artech House.
  22. Soderstorm, T. (2002). Discrete-time stochastic systems, in: Advanced Textbooks in Control and Signal Processing. Springer.
  23. Sugandi, B., Kim, H., Tan, J. K., and Ishikawa, S. (2009). A moving object tracking based on color information employing a particle filter algorithm. In Artificial Life and Robotics. Springer Japan,Vol 14,pp. 39-42.
  24. Wan, E. and van der Merwe, R. (2001). Chapter 7: The unscented kalman filter,. In Kalman Filtering and Neural Networks, S. Haykin, Ed.,. Wiley Publishing.
  25. Zhiqiang, W., Peng, Z., Deng, X., and Li.Shifeng (2011). Particle filter object tracking based on multiple cues fusion. In Advanced in Control Engineering and Information Science. Procedia Engineering,Vol 15, pp. 1461-1465.
  26. Zhonga, Q., Qingqinga, Z., and Tengfeia, G. (2012). Moving object tracking based on codebook and particle filter. In International Workshop on Information and Electronics Engineering. Procedia Engineering,Vol 29,pp. 174-178.

Paper Citation

in Harvard Style

Nagy A., Ahmed A. and H. Zayed H. (2013). Hybrid Iterated Kalman Particle Filter for Object Tracking Problems . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-48-8, pages 375-381. DOI: 10.5220/0004211403750381

in Bibtex Style

author={Amr M. Nagy and Ali Ahmed and Hala H. Zayed},
title={Hybrid Iterated Kalman Particle Filter for Object Tracking Problems},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013)},

in EndNote Style

JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013)
TI - Hybrid Iterated Kalman Particle Filter for Object Tracking Problems
SN - 978-989-8565-48-8
AU - Nagy A.
AU - Ahmed A.
AU - H. Zayed H.
PY - 2013
SP - 375
EP - 381
DO - 10.5220/0004211403750381