Skip to main content
SpringerLink
Log in

Optical flow-based observation models for particle filter tracking

  • Short Paper
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

This paper presents three observation models suitable for particle filter tracking, based on the optical flow of the sequence. Modern optical flow computation techniques can obtain in real time very accurate estimates, so we can use it as a source of evidence for higher level image processing. Our image motion-based models are based, respectively, on: a previously computed optical flow field, the image brightness constraint, and similarity measures. They take into account not only the consistency of the measured optical flow with the motion predicted by the model, but also the presence of optical flow discontinuities on the object boundary. Experimental results show that the resulting trackers are comparable to other, state-of-the-art tracking methods. While the model based on similarity measures provides better performance, the optical flow-field-based model is a suitable option when the flow field is available.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Amiaz T, Kiryati N (2006) Piecewise-smooth dense optical flow via level sets. Int J Comput Vis 68(2):111–124

    Article  Google Scholar 

  2. Avidan S (2004) Support vector tracking. IEEE Trans Pattern Anal Mach Intell 26(8):1064–1072

    Article  Google Scholar 

  3. Babenko B, Yang MH, Belongie S (2011) Visual tracking with online multiple instance learning. IEEE Trans Pattern Anal Mach Intell (PAMI)

  4. Barron JL, Fleet DJ, Beauchemin SS (1994) Performance of optical flow techniques. Int J Comput Vis 12(1):43–77

    Article  Google Scholar 

  5. Bertalmio M, Sapiro G, Randall G (2000) Morphing active contours. IEEE Trans Pattern Anal Mach Intell 22:733–737

    Article  Google Scholar 

  6. Blake A, Isard M (1998) Active contours. Springer, Berlin

    Book  Google Scholar 

  7. Brox T, Bruhn A, Papenberg N, Weickert J (2004) High accuracy optical flow estimation based on a theory for warping. In: Proceedings of the 8th European conference on computer vision, Lecture notes in computer science, vol. 3024. Springer, Berlin, pp. 25–36

  8. Bruhn A, Weickert J (2005) C. Schnörr: Lucas/Kanade meets Horn/Schunck: Combining local and global optic flow methods. Int J Comput Vis 61(3):211–231

    Article  Google Scholar 

  9. Comaniciu D, Ramesh V, Meer P (2003) Kernel-based object tracking. IEEE Trans Pattern Anal Mach Intell 25(5):564–577

    Article  Google Scholar 

  10. Cremers D, Schnrr C (2003) Statistical shape knowledge in variational motion segmentation. Image Vis Comput 21:77–86

    Article  Google Scholar 

  11. Deutscher J, Blake A, North B, Bascle B (1999) Tracking through singularities and discontinuities by random sampling. In: Proceedings of international conference on computer vision, vol. 2, pp 1144–1149

  12. Gelfand A, Smith A (1990) Sampling-based approaches to computing marginal densities. J Am Stat Assoc 85(410):398–409

    Article  MATH  MathSciNet  Google Scholar 

  13. Gwosdek P, Bruhn A, Weickert J (2010) Variational optic flow on the sony playstation 3. J Real-Time Image Process 5:163–177

    Article  Google Scholar 

  14. Horn BKP, Schunck BG (1981) Determining optical flow. Artif Intell 17(1–3):185–203

    Article  Google Scholar 

  15. Kalman R (1960) A new approach to linear filtering and prediction problems. Trans ASME-J Basic Eng 82(Series D):35–45

  16. Kwon J, Lee KM (2010) Visual tracking decomposition. In: CVPR, pp. 1269–1276

  17. Lee D, Choi S (2011) Multisensor fusion-based object detection and tracking using active shape model. In: International conference on digital information management, pp 108–114

  18. Lucas B, Kanade T (1981) An iterative image registration technique with an application to stereo vision. In: Proceedings of DARPA IU Workshop, pp 121–130

  19. Lucena M, Fuertes J, dela Blanca N, Garrido A (2003) An optical flow probabilistic observation model for tracking. In: Proceedings. 2003 International Conference on Image processing, ICIP 2003, vol. 3, pp. III - 957–60 vol. 2

  20. Lucena M, Fuertes JM, laBlanca NP (2004) Evaluation of three optical flow-based observation models for tracking. Int Conf Pattern Recognit 4:236–239

    Google Scholar 

  21. Mansouri A (2002) Region tracking via level set pdes without motion computation. IEEE Trans Pattern Anal Mach Intell 24:947–961

    Article  Google Scholar 

  22. McCane B, Novins K, Crannitch D, Galvin B (2001) On benchmarking optical flow. Comput Vis Image Underst 84:126–143

    Article  MATH  Google Scholar 

  23. Nir T, Bruckstein AM, Kimmel R (2008) Over-parameterized variational optical flow. Int J Comput Vis 76(2):205–216

    Article  Google Scholar 

  24. Proesmans M, VanGool L, Pauwels E, Oosterlinck A (1994) Determination of optical flow and its discontinuities using non-linear diffusion. In: Proceedings of 3rd European conference on computer vision, vol. 2, pp 295–304

  25. Ross DA, Lim J, Lin RS, Yang MH (2008) Incremental learning for robust visual tracking. Int J Comput Vis 77(1–3):125–141

    Article  Google Scholar 

  26. Sullivan J, Blake A, Isard M, MacCormick J (2001) Bayesian object localisation in images. Int J Comput Vis 44(2):111–135

    Article  MATH  Google Scholar 

  27. Watcher S, Nagel HH (1999) Tracking of persons in monocular image sequences. Comput Vis Image Underst 74(3):174–192

    Article  Google Scholar 

  28. Wu Y, Cheng J, Wang J, Lu H, Wang J, Ling H, Blasch E, Bai L (2012) Real-time probabilistic covariance tracking with efficient model update. IEEE Trans Image Process 21(5):2824–2837

    Article  MathSciNet  Google Scholar 

  29. Yang H, Shao L, Zheng F, Wang L, Song Z (2011) Recent advances and trends in visual tracking: a review. Neurocomput. 74(18):3823–3831

    Article  Google Scholar 

  30. Yilmaz A, Javed O, Shah M (2006) Object tracking: s survey. ACM Comput Surv 38(4):13

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Escuela Politecnica Superior, University of Jaen, Campus de las Lagunillas, 23071, Jaén, Spain

    Manuel Lucena & Jose Manuel Fuertes

  2. Department of Computer Science and A.I, University of Granada, 18071, Granada, Spain

    Nicolas Perez de la Blanca

Authors
  1. Manuel Lucena
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jose Manuel Fuertes
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nicolas Perez de la Blanca
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manuel Lucena.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lucena, M., Fuertes, J.M. & de la Blanca, N.P. Optical flow-based observation models for particle filter tracking. Pattern Anal Applic 18, 135–143 (2015). https://doi.org/10.1007/s10044-014-0409-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10044-014-0409-3

Keywords

Search

Navigation