Multimedia Tools and Applications

, Volume 77, Issue 7, pp 8979–9002 | Cite as

Object tracking using distribution fields with correlation coefficients

  • Peng Qin
  • Chi-Man Pun


A real-time object tracking method based on distribution field (DF) constructs with correlation coefficients is proposed to solve the drawbacks of local search and poor real-time performance exhibited by traditional DF tracking methods. With the goal of adapting to complex environments and changes in tracking speed, we propose an algorithm based on DFs and global searching by dense sampling. First, we use the DFs to construct an appearance model that functions as a target descriptor in the particle filter framework, allowing dynamic updating of the appearance model. Then, we measure the similarity using correlation coefficients based on fast Fourier transforms (FFTs) instead of the L1-norm of DFs to reduce the time complexity, overcome the drawback of randomness when using sparse sampling, and avoid falling into local optima from the gradient descent used in traditional DF methods. The results of experiments show that our proposed algorithm not only performs in real time but is also more robust for a variety of complex environments than those of six state-of-the-art algorithms on eight challenging video sequences.


Object tracking Distribution fields Particle filters Correlation coefficients 



This work was supported in part by the Research Committee of the University of Macau (MYRG2015-00011-FST, MYRG2015-00012-FST) and the Science and Technology Development Fund of Macau SAR (093-2014-A2).


  1. 1.
    Babenko B, Ming-Hsuan Y, Belongie S (2011) Robust object tracking with online multiple instance learning. Patt Anal Mach Intell 33(8):1619–1632Google Scholar
  2. 2.
    Baker S, Matthews I (2004) Lucas-Kanade 20 years on: a unifying framework. Int J Comput Vis 56(3):221–255Google Scholar
  3. 3.
    Chenggang Y, Yongdong Z et al (2014) A Highly Parallel framework for HEVC coding unit partitioning tree decision on many-core processors. IEEE Signal Process Lett 21(5):573–576Google Scholar
  4. 4.
    Chenggang Y, Yongdong Z et al (2014) Parallel deblocking filter for HEVC on many-core processor. Electron Lett 50(5):805–806Google Scholar
  5. 5.
    Chenggang Y, Yongdong Z, Feng D, Liang L (2013) Highly parallel framework for HEVC motion estimation on many-core platform. Data Compression Conference (DCC), Snowbird, UT, USAGoogle Scholar
  6. 6.
    Collins RT (2003) Mean-shift blob tracking through scale space. Comp Vis Patt Recog 2:234–240Google Scholar
  7. 7.
    Doucet A, de Freitas N, Gordon N (eds) (2001) Sequential Monte Carlo methods in practice. Springer Verlag, New YorkzbMATHGoogle Scholar
  8. 8.
    Gordon NJ, Salmond DJ, Smith AFM (1993) Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings 140(2):107–113Google Scholar
  9. 9.
    Grabner H, Grabner M, Bischof H (2006) Real-time tracking via on-line boosting. BMVC Conference 1:47–56Google Scholar
  10. 10.
    Grabner H, Leistner C, Bischof H (2008) Semi-supervised on-line boosting for robust tracking. Proc of European Conference on Computer Vision pp 234–247Google Scholar
  11. 11.
    Hager GD, Dewan M, Stewart CV (2006) Multiple kernel tracking with SSD. Comp Vis Patt Recog 1:798–805Google Scholar
  12. 12.
    Han-xuan Y, Feng Z, Liang W, Zhan S, Ling S (2011) Recent advances and trends in visual tracking: a review. J Neurocompt 74(18):3823–3831Google Scholar
  13. 13.
    Heideman MT, Johnson DH, Burrus CS (1984) Gauss and the history of the fast Fourier transform. IEEE ASSP Mag 1(4):14–21zbMATHGoogle Scholar
  14. 14.
    Huang G, Pun C-M, Lin C, Zhou Y (2016) Non-rigid visual object tracking using user-defined marker and Gaussian kernel. Multimed Tools Appl 75(10):5473–5492Google Scholar
  15. 15.
    Ji-feng N, Lei Z, David Z, Chengke W (2012) Robust mean shift tracking with corrected background-weighted histogram. Comp Vis 6(1):62–69MathSciNetGoogle Scholar
  16. 16.
    Kaihua Z, Huihui S (2013) Real-time visual tracking via online weighted multiple instance learning. Pattern Recogn 46(1):397–411zbMATHGoogle Scholar
  17. 17.
    Kai-hua Z, Hui-hui S (2013) Real-time visual tracking via on-line weighted multiple instance learning. Pattern Recogn 46(1):397–411Google Scholar
  18. 18.
    Kaihua Z, Lei Z, Ming-Hsuan Y (2012) Real-time compressive tracking. Eur Conf Comput Vision 3:864–877Google Scholar
  19. 19.
    Kai-hua Z, Lei Z, Ming-hsuan Y (2012) Real-time compressive tracking. Proc of European Conference on Computer Vision pp 864–877Google Scholar
  20. 20.
    Khenouchi H, et al (2016) The Modulus of the Complex Correlation Coefficient Between Co-Polarized Channels for Oil Spill Observation. Living Planet Symposium 740Google Scholar
  21. 21.
    Kitagawa G (1996) Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. J Comput Graph Stat 5(1):1–25MathSciNetGoogle Scholar
  22. 22.
    Koutra D et al (2016) D elta C on: principled massive-graph similarity function with attribution. ACM Transactions on Knowledge Discovery from Data (TKDD) 10.3:28Google Scholar
  23. 23.
    Lin C, Pun C-M, Huang G (2016) Highly non-rigid video object tracking using segment-based object candidates. Multimedia Tools and Applications 76(7):9565–9586Google Scholar
  24. 24.
    Liu JS, Chen R (1998) Sequential Monte Carlo methods for dynamic systems. J Am Stat Assoc 93(443):1032–1044MathSciNetzbMATHGoogle Scholar
  25. 25.
    Maggio E, Cavallaro A (2010) Video tracking: Theroy and practice. John Wiley and Sons, London, pp 15–16Google Scholar
  26. 26.
    Saffari A, Leistner C, Santner J, Godec M, Bischof H (2009) On-line random forests. Proc of International Conference on Computer Vision pp 1393–1400Google Scholar
  27. 27.
    Sanjeev Arulampalam M, Maskell S, Gordon N, Clapp T (2002) A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans Signal Process 50(2):174–188Google Scholar
  28. 28.
    Sevilla-Lara L, Learned-Miller E (2012) Distribution fields for tracking. Comp Vis Patt Recog 34:1910–1917Google Scholar
  29. 29.
    Tang A, Scalzo F (2016) Similarity Metric Learning for 2D to 3D Registration of Brain Vasculature. International Symposium on Visual Computing. Springer International PublishingGoogle Scholar
  30. 30.
    Viola P, Jones M (2001) Rapid object detection using a boosted Cascade of simple features. Comp Vis Patt Recog 1:511–518Google Scholar
  31. 31.
    Wang J, et al (2016) Optimizing top precision performance measure of content-based image retrieval by learning similarity function. arXiv preprint arXiv:1604.06620Google Scholar
  32. 32.
    Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey. Journal of ACM Computing Surveys(CSUR) 38(4):13Google Scholar
  33. 33.
    Yu X, Xiaojun W, Hongyuan W (2012) Object tracking algorithm based on partial feature combination. Opto-Electron Eng 39(7):67–74Google Scholar
  34. 34.
    Yuan X, Yan-yun Q, Cui-hua L (2012) Online multiple instance gradient feature selection for robust visual tracking. Pattern Recogn Lett 39(9):1075–1082Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of Computer and Information ScienceUniversity of MacauMacauChina

Personalised recommendations