Skip to main content

Descriptor-based tracking algorithm using a depth camera

Journal of Communications Technology and Electronics volume 62, pages 638–647 (2017)Cite this article

Abstract

The appearance of inexpensive high-quality video cameras and depth cameras resulted in the development of a large number of object-tracking algorithms. In this paper, a new descriptor-based algorithm for real-time object tracking using the information from a Microsoft Kinect depth camera is proposed. As a descriptor for the object tracked, histograms of oriented gradients calculated from the circular sliding regions of the scene image are used. The information on the depth of the scene is used when the image of the object of interest is partially occluded by other objects in the scene. To speed up the tracking process, a model for predicting the object motion is used. To ensure real-time tracking with the proposed algorithm, a multicore graphics processor is used.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

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

References

  1. A. Yilmaz, O. Javed, and M. Shah, “Object tracking: A survey,” ACM Comput. Surv. 38, 45 (2006).

    Article  Google Scholar 

  2. H. Schweitzer, J. Bell, and F. Wu, “Very fast template matching,” in Proc. 7th Eur. Conf. on Comput. Vis. (ECCV 2002), Copenhagen, Denmark, May 28–31, 2002 (Springer-Verlag, 2002), pp. 358–372.

    Google Scholar 

  3. S. Nejhum, J. Ho, and M. Yang, “Online visual tracking with histograms and articulating blocks,” in Comput. Vis. Image Underst., 2010, pp. 901–914.

    Google Scholar 

  4. V. H. Díaz-Ramírez, K. Picos, and V. Kober, “Target tracking in nonuniform illumination conditions using locally adaptive correlation filters,” Opt. Commun. 323, 32–43 (2014).

    Article  Google Scholar 

  5. I. Haritaoglu, D. Harwood, and L. Davis, “W4: realtime surveillance of people and their activities,” IEEE Trans. Pattern. Anal. Mach. Intell. 22, 809–830 (2000).

    Article  Google Scholar 

  6. F. Talu, I. Turkoglu, and M. Cebeci, “A hybrid tracking method for scaled and oriented objects in crowded scenes,” Expert Syst. Appl. 38, 13682–13687 (2011).

    Google Scholar 

  7. A. Buchanan and A. Fitzgibbon, “Document image dewarping using robust estimation of curled text lines,” Combining local and global motion models for feature point tracking, in Comput. Vision Pat. Recogn., 2007, pp. 1–8.

    Google Scholar 

  8. I. Sbalzarini and P. Koumoutsakos, “Feature point tracking and trajectory analysis for video imaging in cell biology,” J. Struct. Biol. 151 182–195 (2005).

    Article  Google Scholar 

  9. Z. Kalal, K. Mikolajczyk, and J. Matas, “Trackinglearning- detection,” IEEE Trans. Pattern. Anal. Mach. Intell. 34, 1409–1422 (2012).

    Article  Google Scholar 

  10. B. Babenko, Y. C. Ming-Hsuan, and S. Belongie, “Visual tracking with online multiple instance learning,” in Proc. IEEE Conf. on Comput. Vision and Patt. Rec. (CVPR 2009), Miami, Florida, June 20–25, 2009, (IEEE, New York, 2009), pp. 983–990.

    Google Scholar 

  11. S. Song and J. Xiao, “Cluster based weighted SVM for the recognition of Farsi handwritten digits,” in Tracking Revisited Using Rgbd Camera: Unified Benchmark and Base-Lines, (2013), pp. 233–240.

    Google Scholar 

  12. K. Meshgi, S. Maeda, S. Oba, and S. Ishii, “Fusion of multiple cues from color and depth domains using occlusion aware bayesian tracker,” IEICE Tech. Rep. Neurocomp. 114 (500), 127–132 (2014).

    Google Scholar 

  13. N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” Comput. Vis. Patt. Rec. 1, 886–893 (2005).

    Google Scholar 

  14. D. Miramontes-Jaramillo, V. Kober, and V. Díaz-Ramírez, “CWMA: Circular window matching algorithm,” in Proc. 18th Iberoam. Cong. in Patt. Rec., 2013, LNCS 8258, pp. 439–446 (2013).

    Google Scholar 

  15. D. Miramontes-Jaramillo, V. I. Kober, V. H. Díaz-Ramírez, and V. N. Karnaukhov, “A novel Image Matching Algorithm Based on Sliding Histograms of Oriented Gradients,” J. Commun. Technol. Electron. 59, 1446–1450 (2014).

    Article  Google Scholar 

  16. V. Gupta, Nonlinear Filters with Spatially Connected Neighborhoods (Laxmi Publications, 2005).

    Google Scholar 

  17. E. M. Ramos and V. Kober, “Design of correlation filters for recognition of linearly distorted objects in linearly degraded scenes,” J. OSA A 24, 3403–3417 (2007).

    Google Scholar 

  18. L. P. Yaroslavsky and M. Eden, Fundamentals of Digital Optics (Birkhäuse, Boston, 1996).

    Book  MATH  Google Scholar 

  19. L. Po-Ming and C. Hung-Yi, “Adjustable gamma correction circuit for TFT LCD,” in Proc. IEEE Symp. on Circ. and Syst., 2005 (IEEE, New York, 2005), pp. 780–783.

    Chapter  Google Scholar 

  20. W. K. Pratt, Digital Image Processing (Wiley, 2007).

    Book  MATH  Google Scholar 

  21. G. Takacs, V. Chandrasekhar, S. Tsai, R. Grzeszczuk, and B. Girod, “Distortion invariant pattern recognition with local correlations,” Fast Computation of Rotation- Invariant Image Features by Approximate Radial Gradient Transform, 22, No. 8, pp. 2970–2982 (2013).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Sciences, Research and Higher Education (CICESE), Carretera Ensenada-Tijuana 3918, Zona Playitas, 22860, Ensenada, Baja California, Mexico

    D. Miramontes-Jaramillo & V. I. Kober

  2. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, 127051, Russia

    V. I. Kober & V. N. Karnaukhov

  3. Instituto Politecnico Nacional—CITEDI, Ave. del Parque, Mesa de Otay, Tijuana, B.C., 22510, Mexico

    V. H. Díaz-Ramírez

  4. Chelyabinsk State University, ul. Brat’ev Kashirinykh 129, Chelyabinsk, 454001, Russia

    V. I. Kober

Authors
  1. D. Miramontes-Jaramillo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. I. Kober
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. V. H. Díaz-Ramírez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. V. N. Karnaukhov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to D. Miramontes-Jaramillo.

Additional information

Original Russian Text © D. Miramontes-Jaramillo, V.I. Kober, V.H. Díaz-Ramírez, V.N. Karnaukhov, 2016, published in Informatsionnye Protsessy, 2016, Vol. 16, No. 2, pp. 13–26.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Miramontes-Jaramillo, D., Kober, V.I., Díaz-Ramírez, V.H. et al. Descriptor-based tracking algorithm using a depth camera. J. Commun. Technol. Electron. 62, 638–647 (2017). https://doi.org/10.1134/S1064226917060146

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1064226917060146