Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Tracking Multiple Objects with Locally Adaptive Generalized Optimum Correlation Filters

  • THEORY AND METHODS OF INFORMATION PROCESSING
  • Published:
Journal of Communications Technology and Electronics Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract—An algorithm for tracking multiple objects using locally adaptive generalized filtering is proposed. The tracking algorithm is invariant to geometric transformations of objects, partial occlusion of objects, nonuniform illumination of scene, and additive noise in scene images. The proposed system utilizes generalized optimal correlation filters and a prediction scheme based on the kinematic model of objects motion. With the help of iterative training, the training system can be adapted to current scene changes. The performance of the proposed algorithm is compared with that of the state-of-the-art visual tracking algorithms on known databases in terms of commonly accepted quality metrics and processing time.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.

Similar content being viewed by others

REFERENCES

  1. B. Karasulu and S. Korukoglu. Performance Evaluation Software: Moving Object Detection and Tracking in Videos (Springer-Verlag, New York, 2013).

    Book  Google Scholar 

  2. S. Talmale and N. Janwe, “Object tracking in images and videos,” Int. J. Eng. Comp. Sci. 5 (1), 15482−15486 (2016).

    Google Scholar 

  3. B. V. K. Vijaya Kumar, J. A. Fernandez, A. Rodriguez, and V. N. Boddeti, “Recent advances in correlation filter theory and application,” Proc. SPIE 9094, 909404−13 (2014).

    Article  Google Scholar 

  4. B. V. K. V. Kumar, A. Mahalanobis, and R. D. Juday, Correlation Pattern Recognition (Cambridge Univ. Press, New York, 2005).

    Book  Google Scholar 

  5. A. W. M. Smeulders, D. M. Chu, R. Cucchiara, et al., “Visual tracking: An experimental survey,” IEEE Trans. Pattern Anal. Mach. Intell. 36, 1442−1468 (2014).

    Article  Google Scholar 

  6. O. Akin, E. Erdem, A. Erdem, and K. Mikolajczyk, “Deformable part-based tracking by coupled global and local correlation filters,” J. Visual Commun. Image Representation 38 (Suppl. C), 763−774 (2016).

    Article  Google Scholar 

  7. A. Ali, A. Jalil, J. Ahmed, et al., “Correlation, Kalman filter and adaptive fast mean shift based heuristic approach for robust visual tracking,” Signal, Image and Video Process. 9, 1567−1585 (2015).

    Article  Google Scholar 

  8. J. F. Henriques, R. Caseiro, P. Martins, and J. Batista, “High-Speed Tracking with Kernelized Correlation Filters,” IEEE Trans. Pattern Analysis & Machine Intell. 37 (3), 583−596 (2015).

    Article  Google Scholar 

  9. X. Li, Q. Liu, Z. He, et al., “A multi-view model for visual tracking via correlation filters,” Knowledge-Based Systems 113 (Suppl. C), 88−99 (2016).

    Article  Google Scholar 

  10. Z. Li, J. Yang, J. Zha, et al., “Online visual tracking via correlation filter with convolutional networks,” in Visual Communications and Image Processing (VCIP 2016) (Proc. Meeting, Chengdu, China, Nov. 27−30,2016) (IEEE, New York, 2016), pp. 1−4.

  11. F. Liu, T. Zhou, K. Fu, and J. Yang, “Robust visual tracking via constrained correlation filter coding,” Pattern Recogn. Lett. 84 (Suppl. C), 163−169 (2016).

    Article  Google Scholar 

  12. F. Liu, T. Zhou, and J. Yang, “Geometric affine transformation estimation via correlation filter for visual tracking,” Neurocomputing 214 (Suppl. C), 109−120 (2016).

    Article  Google Scholar 

  13. S. Jeong, G. Kim, and S. Lee, “Effective visual tracking using multi-block and scale space based on kernelized correlation filters,” Sensors 17, 433 (2017).

    Article  Google Scholar 

  14. A. Bibi, M. Mueller, and B. Ghanem, “Target response adaptation for correlation filter tracking, in Computer Vision—ECCV 2016 (Proc. 14th Eur. Conf., Amsterdam, The Netherlands, Oct. 1−14,2016, Part VI) (Springer Int., Amsterdam, 2016), pp. 419−433.

  15. E. Gundogdu, H. Ozkan, and A. A. Alatan, “Extending correlation filter-based visual tracking by treestructured ensemble and spatial windowing,” IEEE Trans. Image Process. 26, 5270−5283 (2017).

    Article  MathSciNet  Google Scholar 

  16. S. E. Ontiveros-Gallardo and V. Kober, “Objects tracking with adaptive correlation filters and Kalman filtering,” Proc. SPIE 9598, 95980 (2015).

    Google Scholar 

  17. V. H. Daz-Ramrez, K. Picos, and V. Kober, “Object tracking in nonuniform illumination using space-variant correlation filters,” in Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications (Proc.18th Iberoamerican Congress, CIARP 2013, Havana, Cuba, Nov. 20−23, 2013, Part II) (Springer-Verlag, Berlin, 2013).

  18. V. H. Diaz-Ramirez, 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 

  19. V. H. Diaz-Ramirez, V. Contreras, V. Kober, and K. Picos, “Real-time tracking of multiple objects using adaptive correlation filters with complex constraints,” Opt. Commun. 309, 265−278 (2013).

    Article  Google Scholar 

  20. N. Leopoldo, VictorH. Gaxiola, and J. J. T. Diaz-Ramirez, “Target tracking using interest point detection and correlation filtering,” Proc. SPIE 9217, 9217−8 (2014).

    Google Scholar 

  21. L. N. Gaxiola, V. H. Daz-Ramrez, J. J. Tapia, et al., “Robust face tracking with locally-adaptive correlation filtering,” in Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications (Proc. 19th Iberoamerican Congress, CIARP 2014, Puerto Vallarta, Mexico, November 2−5,2014) (CIARP, 2014), pp. 925−932.

  22. A. Cuevas, V. H. Diaz-Ramirez, V. Kober, and L. Trujillo, “Facial recognition using composite correlation filters designed with multiobjective combinatorial optimization,” Proc. SPIE 9217, 921710−8 (2014).

    Article  Google Scholar 

  23. L. N. Gaxiola, V. H. Diaz-Ramirez, J. J. Tapia, and P. Garcia-Martinez, “Target tracking with dynamically adaptive correlation,” Opt. Commun. 365, 140−149 (2016).

    Article  Google Scholar 

  24. A. Ruchay and V. Kober, “A correlation-based algorithm for recognition and tracking of partially occluded objects,” Proc. SPIE 9971, 99712 (2016).

    Google Scholar 

  25. A. Ruchay, V. Kober, and J. A. Gonzalez-Fraga, “Reliable recognition of partially occluded objects with correlation Filters,” Math. Problems Eng. 2018, 8284123 (2018).

    Article  Google Scholar 

  26. P. M. Aguilar-Gonzalez and V. Kober, “Design of correlation filters for pattern recognition with disjoint reference image,” Opt. Eng. 50, 117201 (2011).

    Article  Google Scholar 

  27. P. M. Aguilar-Gonzalez and V. Kober, “Design of correlation filters for pattern recognition using a noisy reference,” Opt. Commun. 285, 574−583 (2012).

    Article  Google Scholar 

  28. E. M. Ramos-Michel and V. Kober, “Adaptive composite filters for pattern recognition in linearly degraded and noisy scenes,” Opt. Eng. 47, 047204−7 (2008).

    Article  Google Scholar 

  29. P.-M. Lee and H.-Y. Chen, “Adjustable gamma correction circuit for Tft Lcd,” in Proc. IEEE Int. Symp. on Circuits and Systems, Kobe, Japan,2005 (IEEE, New York, 2005), pp. 780−783.

  30. P. Gupta, Comprehensive Business Statistics (Laxmi Publ., 2005).

    Google Scholar 

  31. Y. Wu, J. Lim, and M. H. Yang, “Object tracking benchmark,” IEEE Trans. Pattern Analysis & Machine Intell. 37, 1834−1848 (2015).

    Article  Google Scholar 

  32. T. Al-Asadi and A. Obaid, “Object detection and recognition by using enhanced speeded up robust feature,” Int. J. Comp. Sci. Network Security 16 (4), 66−71 (2016).

    Google Scholar 

  33. P. H. S. Torr, S. Hare, and A. Saffari, “Struck: Structured output tracking with kernels,” in Proc. IEEE Int. Conf. on Computer Vision (ICCV 2011), Barcelona, Spain, Nov. 2011 (IEEE, New York, 2011), pp. 263−270.

    Google Scholar 

  34. W. Zhong, “Robust object tracking via sparsity-based collaborative model,” in Proc. 2012 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR’12), Providence, RI, June 2012 (IEEE Computer Soc., Washington, 2012), pp. 1838−1845.

  35. S. E. Ontiveros-Gallardo and V. Kober, “Correlation-based tracking using tunable training and Kalman prediction,” Proc. SPIE 9971, 997129−9 (2016).

    Article  Google Scholar 

Download references

Funding

This work was supported by the Russian Science Foundation, project no. 17-76-20045.

Author information

Authors and Affiliations

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

    V. I. Kober & V. N. Karnaukhov

  2. Chelyabinsk State University, 454001, Chelyabinsk, Russia

    V. I. Kober & A. N. Ruchay

  3. Department of Computer Science, CICESE, 22860, Ensenada B.C., Mexico

    V. I. Kober

  4. Federal Scientific Center for Biological Systems and Agrotechnologies Russian Academy of Sciences, 460000, Orenburg, Russia

    A. N. Ruchay

Authors
  1. V. I. Kober
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. N. Ruchay
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. V. N. Karnaukhov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to V. I. Kober.

Additional information

Translated by E. Oborin

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kober, V.I., Ruchay, A.N. & Karnaukhov, V.N. Tracking Multiple Objects with Locally Adaptive Generalized Optimum Correlation Filters. J. Commun. Technol. Electron. 65, 716–724 (2020). https://doi.org/10.1134/S1064226920060169

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1064226920060169

Search

Navigation