Skip to main content
SpringerLink
Log in

Mean LBP and modified fuzzy C-means weighted hybrid feature for illumination invariant mean-shift tracking

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Object tracking is a critical task in surveillance and activity analysis. Two main issues for tracking are appearance (illumination) and structural (size of a target) variations of the object. We propose a method which is robust and addresses these issues by incorporating features that are less variant to these changes. The proposed features are mean local binary pattern (mLBP), an illumination invariant texture feature, and modified fuzzy c-means (MFCM) weighted color histogram to handle both illumination and scale changes. These features are combined to form a hybrid mean-shift (MS) vector and used in the MS vector framework for target tracking. Experimental results using standard benchmark videos show that the proposed scheme can lead to better localization and robust tracking in challenging illumination scenarios, when compared to several existing tracking algorithms.

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. AVSS: http://www.eecs.qmul.ac.uk/~andrea/avss2007_d.html. (2007)

  2. CAVIAR data: http://homepages.inf.ed.ac.uk/rbf/CAVIAR/

  3. PETS: http://www.cvg.reading.ac.uk/PETS2009, (2009)

  4. Ali, A., Jalil, A., Ahmed, J., Iftikhar, M., Hussain, M.: Correlation, Kalman filter and adaptive fast mean shift based heuristic approach for robust visual tracking. In: Springer Proceedings Signal, Image and Video Processing, (2015)

  5. Cannons, K. J., Richard, W.: Spatiotemporal oriented energy features for visual tracking. In: Springer ACCV, Lecture Notes in Computer Science (2007)

  6. C.Bao, Wu.Yi, Hui, H.: Real time robust l1 tracker using accelerated proximal gradient approach. In: IEEE Proceedings CVPR, (2012)

  7. Cheng, F.C., Chen, B.H., Huang, S.C.: A hybrid background subtraction method with background and foreground candidates detection. ACM Trans. Intell. Syst. Technol (2015)

  8. Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean shift. In: IEEE Proceedings CVPR, (2000)

  9. Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. In: IEEE Transactions Pattern Analysis and Machine Intelligence, (2003)

  10. Deilamani, M., Asli, R.: Moving object tracking based on mean shift algorithm and features fusion. In: International Conference AISP, (2011)

  11. Ross, D., Lim, M-H.: Incremental learning for robust visual tracking. In: Springer Journal Computer Vision, (2008)

  12. Freedman, D., Turek, W.: Illumination-invariant tracking via graph cuts. In: IEEE Proceedings CVPR, (2005)

  13. Hong, L., Ze, Y., Hongbin, Z., Yuexian, Z., Zhang, L.: Robust human tracking based on multi-cue integration and mean-shift. In: Elsevier Science Inc. Pattern Recognition Letters, (2009)

  14. Huang, K., Wang, L., Tan, T., Maybank, S.: A real-time object detecting and tracking system for outdoor night surveillance. In: Sciencedirect Proceedings Pattern Recognition, (2008)

  15. Ning, J., Zhang, L., Zhang, D., Wu, C.: Robust object tracking using joint color-texture histogram. Pattern Recogn. Artif. Intell. 23(7), 1245–1263 (2009)

    Article  Google Scholar 

  16. Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Comput. Surv. 38(4), 13 (2016). doi:10.1145/1177352.1177355

  17. Koohzadi, M., Keyvanpour, M.: Otwc: an efficient object-tracking method. Signal Image Video Process. 9, 6 (2015)

    Article  Google Scholar 

  18. Kwon, J., Lee, K.: Visual tracking decomposition. In: IEEE Proceedings CVPR, (2010)

  19. Zhang, K., Zhang, L., Ming-Hsuan, Y.: Real-time compressive tracking. In: Springer Proceedings ECCV 2012, (2012)

  20. Lehuger, A., Patrick, L., Patrick, P.: An adaptive mixture color model for robust visual tracking. In: IEEE Proceedings ICIP, (2006)

  21. Lim, J., Ross, D., Ruei-Sung, L., M.Yang: Incremental learning for visual tracking. In: Advances in Neural Information Processing Systems, (2005)

  22. Bales, M., Forsthoefel, D., Wills, D., ScottWills, D., Wills, L.: Illumination change compensation techniques to improve kinematic tracking. In: IEEE WACV, (2011)

  23. Bales, M., Ryan, F.: Bigbackground-based illumination compensation for surveillance video. In: Hindawi Proceedings Image Video Processing, (2011)

  24. Mckenna, S., Raja, Y., Gong, S.: Object tracking using adaptive colour mixture models. In: Asian Conference on Computer Vision, (1998)

  25. Miller, A., Shah, M.: Person and vehicle tracking in surveillance video. Springer Lecture Notes in Computer Science, (2007)

  26. Nawaz, T., Cavallaro, A.: PFT: a protocol for evaluating video trackers. In: IEEE Proceedings ICIP, (2011)

  27. Ning, J., Zhang, L., Zhang, D., Wu, C.: Robust mean-shift tracking with corrected background-weighted histogram. In: IET Computer Vision, (2012)

  28. Nummiaro, V., Koller-Meier, E.: An adaptive color based particle filter. In: Elsevier Science image and vision computing (2003)

  29. Ojala, T., Valkealahti, K., Oja, E.: Multiresolution gray scale and rotation invariant texture analysis with local binary patterns. In: IEEE Transaction Pattern Analysis and Machine Intelligence, (2002)

  30. Phadke, G., Velmurugan, R.: Illumination invariant mean-shift tracking. In: IEEE WACV, (2013)

  31. Sarkar, R., Das, S., Vaswani N.: Pafimocs: particle filtered modified-cs and applications in visual tracking across illumination change. In: arXiv:1301.1374 [cs.CV] (2013)

  32. Huang, S., Cheng, F., Chiu, Y.: Efficient contrast enhancement using adaptive gamma correction with weighting distribution. In: IEEE Transaction Image Processing, (2013)

  33. Sevilla-Lara, L., Miller, E.: Distribution fields for tracking. In: IEEE Proceedings CVPR, (2012)

  34. Shan, D., Zhang, C.: Visual tracking using ipca and sparse representation. Signal, Image Video Process. (2015)

  35. Rautaray, S., Agrawal, A.: Hybrid wavelet-based video tracking using adaptive contrast change detection in night-time visual surveillance systems. In: The World Congress on Engineering, (2010)

  36. Sun, X., Zhang, J., Xie, Z., Gao, J., Wang, L., Heidingsfelder, P.: Active-matting-based object tracking with color cues. In: Springer Proceedings Signal, Image, and video Processing, (2014)

  37. Suryanto, D., Kim, H., SungJea, K.: Spatial color histogram based center voting method for subsequent object tracking and segmentation. In: Image and Vision Computing, (2011)

  38. Wang, J., Yagi, Y.: Integrating color and shape-texture features for adaptive real-time object tracking. IEEE Transactions Image Processing, (2008)

  39. Wang X-Y, Zhang, X.J.: A pixel-based color image segmentation using support vector machine and fuzzy c-means. In: Elsevier Science Neural Network, (2012)

  40. Wu, H., Liu, N., Luo, X., Su, J., Chen, L.: Real-time background subtraction-based video surveillance of people by integrating local texture patterns. Signal, Image and Video Processing, (2014)

  41. Wu, Y., Lim, J., Yang, M.: Online object tracking: A benchmark. In: IEEE conference on CVPR, (2013)

  42. Y.Rubner, Tomasi, C., Guibas, L.: The earth mover’s distance as a metric for image retrieval. In: International Journal of Computer Vision, (2000)

  43. Yu, G., Lu, H.: Illumination invariant object tracking with incremental subspace learning. In: International conference ICIG, (2009)

  44. Kalal, Z., Matas, J., Mikolajczyk, K.: P-n learning: Bootstrapping binary classifiers by structural constraints. In: IEEE Proceedings CVPR, (2010)

  45. Tianzhu, Z., Ghanem, B.: Robust visual tracking via multi-task sparse learning. In: IEEE conference on CVPR (2012)

Download references

Author information

Authors and Affiliations

  1. Indian Institute of Technology Bombay, Mumbai, 400076, India

    Gargi Phadke & Rajbabu Velmurugan

Authors
  1. Gargi Phadke
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rajbabu Velmurugan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gargi Phadke.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Phadke, G., Velmurugan, R. Mean LBP and modified fuzzy C-means weighted hybrid feature for illumination invariant mean-shift tracking. SIViP 11, 665–672 (2017). https://doi.org/10.1007/s11760-016-1008-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-016-1008-0

Keywords

Search

Navigation