Skip to main content
SpringerLink
Log in

A novel background-weighted histogram scheme based on foreground saliency for mean-shift tracking

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Effective appearance models are one critical factor for robust object tracking. In this paper, we introduce foreground feature saliency concept into the background modelling, and put forward a novel foreground saliency-based background-weighted histogram scheme (FSBWH) for target representation and tracking, which exploits salient features from both foreground and background. We think that background and foreground salient features are both crucial for target representation and tracking. Experimental results show that the proposed FSBWH scheme can improve the robustness and performance of tracker significantly especially in complex occlusions and similar background scenes.

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
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Notes

  1. An early version of this work was presented in [31].

References

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

    Article  Google Scholar 

  2. Bao C, Wu Y, Ling H et al (2012) “Real time robust L1 tracker using accelerated proximal gradient approach”. IEEE Conf Comput Vis Patt Recognit 1830–1837

  3. Birchfield ST, Rangarajan S (2005) “Spatiograms versus histograms for region-based tracking”. IEEE Comput Soc Conf Comput Vis Pattern Recognit 1158–1163

  4. Bolme D, Beveridge J, Draper B. et al (2010) “Visual object tracking using adaptive correlation filters”. IEEE Conf Comput Vision Pattern Recognit (CVPR) 2544–2550

  5. CAVIAR Test Case Scenarios, http://www.homepages.inf.ed.ac.uk/rbf/ CAVIAR

  6. Chu H (2013) “Mean shift target tracking with spatiogram corrected background-weighted histogram”. Control Decis 28(3)

  7. Comaniciu D, Ramesh V, Meer P et al (2000) “Real-time tracking of non-rigid objects using mean shift”. IEEE Conf Comput Vision Pattern Recognit(CVPR) 142–149

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

    Article  Google Scholar 

  9. Danelljan M, Khan FS, Felsberg M et al (2014) “Adaptive color attributes for real-time visual tracking”. IEEE Conf Comput Vision Pattern Recognit (CVPR) 1090–1097

  10. Fujii K, Salerno A, Sriskandarajah K et al (2013) “Gaze contingent cartesian control of a robotic arm for laparoscopic surgery”. IEEE/RSJ Int Conf Intell Robots Syst (IROS) 3582-3589

  11. Henriques J, Caseiro R, Martins P et al (2012) “Exploiting the circulant structure of tracking-by-detection with kernels”. 12th Europ Conf Comput Vision 702–715

  12. Kwak S, Nam W, Han B et al (2011) “Learning occlusion with likelihoods for visual tracking”. IEEE Int Conf Comput Vis (ICCV) 1551–1558

  13. Li S, Wu O, Zhu C et al (2014) Visual object tracking using spatial context information and global tracking skills. Comput Vis Image Underst 125:1–15

  14. Li X, Dick A, Wang H et al (2011) “Graph mode-based contextual kernels for robust SVM tracking”, IEEE Int Conf Comput Vision (ICCV), pp. 1156-1163, 2011.

  15. Li Y, Ai H, Yamashita T et al (2008) Tracking in low frame rate video: a cascade particle filter with discriminative observers of different life spans. IEEE Trans Pattern Anal Mach Intell 30(10):1728–1740

  16. Mei X, Ling H, Wu Y et al (2011) “Minimum error bounded efficient L1 tracker with occlusion detection”, IEEE Conf Comput Vis Pattern Recognit (CVPR) 257–1264

  17. Mikami D, Otsuka K, Yamato J. et al (2010) “Memory-based particle filter for tracking objects with large variation in pose and appearance”. 11th Europ Conf Comput Vision 215–228

  18. Moroni D, Pieri G (2009) Object tracking in video-surveillance”. Patt Recognit Imag Anal 19(2):271–276

    Article  Google Scholar 

  19. Morwald T, Zillich M, Prankl J et al (2011) “Self-monitoring to improve robustness of 3D object tracking for robotics”. IEEE Int Conf Robotics Biomimetics (ROBIO) 2830–2837

  20. Mure S, Grenier T, Meier D et al (2015) “Unsupervised spatio-temporal filtering of image sequences A mean-shift specification”. Pattern Recognit Lett

  21. Ning J, Zhang L, Zhang D et al (2010) “Robust mean-shift tracking with corrected background-weighted histogram”. IET Comput Vis

  22. Ning J, Zhang L, Zhang D, Wu C (2012) “Scale and orientation adaptive mean shift tracking”. IET Comput Vis 6(1):52

    Article  MathSciNet  Google Scholar 

  23. Olesen OV, Paulsen RR, Hojgaard L, Roed B, Larsen R (2012) Motion tracking for medical imaging: a nonvisible structured light tracking approach”. IEEE Trans Med Imaging 31(1):79–87

    Article  Google Scholar 

  24. Pérez P, Hue C, Vermaak J et al (2002) “Color-based probabilistic tracking”. 7th Europ Conf Comput Vision 661-675

  25. PETS 2001 dataset. http://www.cvg.cs.rdg.ac.uk/ PETS2001/

  26. Rivlin E, Adam ISA (2006) “Robust fragments-based tracking using the integral histogram”. IEEE Int Conf Comput Vis Pattern Recognit 798–805

  27. Tian M, Zhang W, Liu F.et al (2007) “On-line ensemble SVM for robust Object tracking” 8th Asian Conf Comput Vision 355–364

  28. Vojir T, Noskova J, Matas J (2014) Robust scale-adaptive mean-shift for tracking”. Pattern Recogn Lett 49:250–258

    Article  Google Scholar 

  29. Wang D, Lu H, Chen Y et al (2010) “Object tracking by multi-cues spatial pyramid matching”. 17th IEEE Int Conf Imag Process (ICIP) 3957–3960

  30. Wang D, Shi Y, Sun W et al (2014) Object tracking with a novel method based on fs-cbwh within mean-shift framework. 11th Int Sympos Neural Networks (ISNN) 508–515

  31. Wang D, Sun W, Chen K, Yu S (2014) Efficient mean-shift tracking using an improved weighted-histogram scheme. KSII Trans Internet Inform Syst (TIIS) 8(6):1964–1981

    Article  Google Scholar 

  32. Wang L, Pan C, Xiang S et al (2011) Mean-shift tracking algorithm with weight fusion strategy. Imag Process (ICIP) 473--476. doi:10.1109/ICIP.2011.6116554

  33. Zheng H, Mao X, Chen L et al (2015) “Adaptive edge-based mean shift for drastic change gray target tracking”. Optik - Int J Light Electron Optics

Download references

Acknowledgments

This work is supported by the National Natural Science Foundation of China (NSFC) under Grant 61300140.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Huazhong University of Science and Technology , Wuhan, 430074, China

    Dejun Wang, Weiping Sun, Shengsheng Yu, Lin Li & Wei Liu

  2. Donghua Software Co., Ltd., Beijing, 100190, China

    Dejun Wang

  3. School of Computer Science and Technology, Wuhan University of Technology, Wuhan, 430070, China

    Lin Li

  4. School of Computer Science, Central China Normal University, Wuhan, 430079, China

    Wei Liu

Authors
  1. Dejun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weiping Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shengsheng Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wei Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Weiping Sun.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, D., Sun, W., Yu, S. et al. A novel background-weighted histogram scheme based on foreground saliency for mean-shift tracking. Multimed Tools Appl 75, 10271–10289 (2016). https://doi.org/10.1007/s11042-015-3078-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-015-3078-3

Keywords

Search

Navigation