Advertisement

MATRIOSKA: A Multi-level Approach to Fast Tracking by Learning

  • Mario Edoardo Maresca
  • Alfredo Petrosino
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8157)

Abstract

In this paper we propose a novel framework for the detection and tracking in real-time of unknown object in a video stream. We decompose the problem into two separate modules: detection and learning. The detection module can use multiple keypoint-based methods (ORB, FREAK, BRISK, SIFT, SURF and more) inside a fallback model, to correctly localize the object frame by frame exploiting the strengths of each method. The learning module updates the object model, with a growing and pruning approach, to account for changes in its appearance and extracts negative samples to further improve the detector performance. To show the effectiveness of the proposed tracking-by-detection algorithm, we present quantitative results on a number of challenging sequences where the target object goes through changes of pose, scale and illumination.

Keywords

Tracking by detection real-time keypoint-based methods learning interest points 

References

  1. 1.
    Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. Int. J. Comput. Vision 60, 91–110 (2004)CrossRefGoogle Scholar
  2. 2.
    Ke, Y., Sukthankar, R.: PCA-SIFT: a more distinctive representation for local image descriptors. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 506–513 (2004)Google Scholar
  3. 3.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-Up Robust Features (SURF). Comput. Vis. Image Underst. 110, 346–359 (2004)CrossRefGoogle Scholar
  4. 4.
    Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: An efficient alternative to SIFT or SURF. In: 2011 IEEE International Conference on Computer Vision, pp. 2564–2571 (2011)Google Scholar
  5. 5.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 1615–1630 (2005)CrossRefGoogle Scholar
  6. 6.
    Leutenegger, S., Chli, M., Siegwart, R.Y.: BRISK: Binary Robust invariant scalable keypoints. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2548–2555 (2011)Google Scholar
  7. 7.
    Morel, J.-M., Yu, G.: ASIFT: A New Framework for Fully Affine Invariant Image Comparison. SIAM J. Img. Sci. 2, 438–469 (2009)CrossRefzbMATHMathSciNetGoogle Scholar
  8. 8.
    Ortiz, R.: FREAK: Fast Retina Keypoint. In: Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 510–517 (2012)Google Scholar
  9. 9.
    Alcantarilla, P.F., Bartoli, A., Davison, A.J.: KAZE features. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VI. LNCS, vol. 7577, pp. 214–227. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  10. 10.
    Yilmaz, A., Javed, O., Shah, M.: Object tracking: A survey. ACM Comput. Surv. 38 (2006)Google Scholar
  11. 11.
    Kloihofer, W., Kampel, M.: Interest Point Based Tracking. In: 2010 20th International Conference on Pattern Recognition, pp. 3549–3552 (2010)Google Scholar
  12. 12.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 511–518 (2001)Google Scholar
  13. 13.
    Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-Learning-Detection. IEEE Trans. on Pattern Anal. Mach. Intell. 34, 1409–1422 (2012)CrossRefGoogle Scholar
  14. 14.
    Babenko, B., Yang, M.-H., Belongie, S.: Robust Object Tracking with Online Multiple Instance Learning. IEEE Trans. Pattern Anal. Mach. Intell. 33, 1619–1632 (2011)CrossRefGoogle Scholar
  15. 15.
    Ross, D.A., Lim, J., Lin, R.-S., Yang, M.-H.: Incremental Learning for Robust Visual Tracking. Int. J. Comput. Vision 77, 125–141 (2008)CrossRefGoogle Scholar
  16. 16.
    Hare, S., Saffari, A., Torr, P.H.S.: Efficient online structured output learning for keypoint-based object tracking. In: Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1894–1901 (2012)Google Scholar
  17. 17.
    Heinly, J., Dunn, E., Frahm, J.-M.: Comparative evaluation of binary features. In: Proceedings of the 12th European Conference on CV, pp. 759–773 (2012)Google Scholar
  18. 18.
    Gauglitz, S., Höllerer, T., Turk, M.: Evaluation of Interest Point Detectors and Feature Descriptors for Visual Tracking. Int. J. Comput. Vision 94, 335–360 (2011)CrossRefzbMATHGoogle Scholar
  19. 19.
    Khvedchenia, I.: A battle of three descriptors: SURF, FREAK and BRISK (2012), http://computer-vision-talks.com/
  20. 20.
    Godec, M., Roth, P.M., Bischof, H.: Hough-based tracking of non-rigid objects. In: Proceedings of the 2011 International Conference on Computer Vision, pp. 81–88 (2011)Google Scholar
  21. 21.
    Yu, Q., Dinh, T.B., Medioni, G.G.: Online Tracking and Reacquisition Using Co-trained Generative and Discriminative Trackers. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 678–691. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  22. 22.
    Avidan, S.: Ensemble Tracking. IEEE Trans. Pattern Anal. Mach. Intell. 29, 261–271 (2007)CrossRefGoogle Scholar
  23. 23.
    Santner, J., Leistner, C., Saffari, A., Pock, T., Bischof, H.: PROST Parallel Robust Online Simple Tracking. In: 2010 IEEE Conference on CVPR, pp. 723–730 (2010)Google Scholar
  24. 24.
    Grabner, H., Bischof, H.: On-line Boosting and Vision. In: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 260–267 (2006)Google Scholar
  25. 25.
    Grabner, H., Leistner, C., Bischof, H.: Semi-supervised On-Line Boosting for Robust Tracking. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 234–247. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  26. 26.
    Pernici, F.: FaceHugger: The ALIEN Tracker Applied to Faces. In: Fusiello, A., Murino, V., Cucchiara, R. (eds.) ECCV 2012 Ws/Demos, Part III. LNCS, vol. 7585, pp. 597–601. Springer, Heidelberg (2012)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Mario Edoardo Maresca
    • 1
  • Alfredo Petrosino
    • 1
  1. 1.Department of Science and TechnologyUniversity of Naples ParthenopeNapoliItaly

Personalised recommendations