Advertisement

Feature-Based Tracking via SURF Detector and BRISK Descriptor

  • Sangeen KhanEmail author
  • Sehat Ullah
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 810)

Abstract

Marker-less tracking has become vital for a variety of vision-based tasks because it tracks some natural regions of the object rather than using fiducial markers. In marker-less feature-based tracking salient regions in the images are identified by the detector and information about these regions are extracted and stored by the descriptor for matching. Speeded-Up Robust Feature (SURF) is considered as the most robust detector and descriptor so far. SURF detects the feature points that are unique and repeatable. It uses integral images which provide a base for the low computational expense. However, descriptors generation and matching for SURF is a time-consuming task. Binary Robust Invariant Scalable Key-points (BRISK) is a scale and rotation invariant binary descriptor. It reduces the computational cost due to its binary nature. This paper presents a marker-less tracking system that tracks natural features of the object in real-time and is very economical in terms of computation. The proposed system is based on SURF detector, as it identifies highly repeatable interest points in the object and BRISK descriptor, due to its low computational cost and invariance to scale and rotation which is vital for every visual tracking system.

Keywords

Features Detection Description Matching Tracking 

References

  1. 1.
    Rabbi, I., Ullah, S.: A survey on augmented reality challenges and tracking. Acta Graphica 24(1–2), 29–46 (2013)Google Scholar
  2. 2.
    Sridhar, S., Kamat, V.R.: A real-time markerless camera pose estimation system for augmented reality. UMCEE Report. University of Michigan, Ann Arbor (2011)Google Scholar
  3. 3.
    Kim, D., Moon, W., Kim, S.: A study on method of advanced marker array. IJSEIA 8(6), 1–16 (2014)CrossRefGoogle Scholar
  4. 4.
    Kusuma, G.P., Teck, F.W., Yiqun, L.: Hybrid feature and template-based tracking for augmented reality application. In: Asian Conference on Computer Vision, pp. 381–395 (2014)Google Scholar
  5. 5.
    Derntl, A.: Survey of feature detectors and descriptors in surgical domain. In: IEEE GSC, (2014)Google Scholar
  6. 6.
    Weng, E.N.G., et al.: Objects tracking from natural features in mobile augmented reality. Procedia-Soc. Behav. Sci. 97, 753–760 (2013)CrossRefGoogle Scholar
  7. 7.
    Demiroz, B.E., Ari, I., Eroglu, O., Salah, A.A., Akarun, L.: Feature-based tracking on a multi-omnidirectional camera dataset. In: International Symposium on Communications Control and Signal Processing, pp. 1–5 (2012)Google Scholar
  8. 8.
    Zhang, F., Lasluisa, S., Jin, T., Rodero, I., Bui, H., Parashar M.: In-situ feature-based objects tracking for large-scale Scientific simulations. In: High-Performance Computing, Networking, Storage and Analysis (SCC), pp. 736–740 (2012)Google Scholar
  9. 9.
    Monisha, R., Muthuselvam, M.: Feature based moving object detection and tracking. In: International Conference on Innovative Trends in Engineering and Technology (2017)Google Scholar
  10. 10.
    Shisode, S.P., Moholkar, K.P.: Real-time object identification, training and matching via SURF algorithm methods. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 4(6) (2014)Google Scholar
  11. 11.
    Bay, H., Ess, A., Tuytelaars, T., Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)CrossRefGoogle Scholar
  12. 12.
    Miksik, O., Mikolajczyk, K.: Evaluation of local detectors and descriptors for fast feature matching. In: International Conference on Pattern Recognition, Tsukuba Science City Japan, pp. 2681–2684, Nov 2012Google Scholar
  13. 13.
    Wang, W., Zhou, Y., Zhu, X., Xing, Y.: A real-time tracking method based on SURF. In: International Congress on Image and Signal Processing (CSIP), pp. 325–329. IEEE (2015)Google Scholar
  14. 14.
    El-Gayar, M.M., Soliman, H., Meky, N.: A comparative study of image low-level feature extraction algorithms. Egypt. Inf. J. 14(2), 175–181 (2013)CrossRefGoogle Scholar
  15. 15.
    Du, G., Su, F., Cai, A.: Face recognition using SURF features. In: Proceedings of SPIE, vol. 7496, pp. 749628–1, Oct 2009Google Scholar
  16. 16.
    Gil’s Computer vision blog. https://gilscvblog.com. Accessed 31 Jan 2017
  17. 17.
    Kashif, M., Deserno, T.M., Haak, D., Jonas, S.: Feature description with SIFT, SURF, BRIEF, BRISK, or FREAK? A general question answered for bone age assessment. Comput. Biol. Med. 68, 67–75 (2016)CrossRefGoogle Scholar
  18. 18.
    Leutenegger, S., Chli, M., Siegwart, R.Y.: BRISK: binary robust invariant scalable keypoints. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2548–2555, Nov 2011Google Scholar
  19. 19.
    Heinly, J., Dunn, E., Frahm, J.M.: Comparative evaluation of binary features. In: Proceedings of the 12th European Conference on Computer Vision, pp. 759–773 (2012)Google Scholar
  20. 20.
    Swain, M.J., Ballard, D.H.: Color indexing. Int. J. Comput. Vis. 7(1), 11–32 (1991)Google Scholar
  21. 21.
    Harris, C., Stephens, M.: A combined corner and edge detector. Alvery Vis. Conf. 15(50), 10–5244 (1988)Google Scholar
  22. 22.
    Smith, S.M., Brady, J.M.: SUSAN—a new approach to low-level image processing. Int. J. Comput. Vis. 23(1), 45–78 (1997)CrossRefGoogle Scholar
  23. 23.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Obdrzalek, S., Matas, J.: Object recognition using local affine frames on distinguished regions. BMVC 1, 3 (2002)Google Scholar
  25. 25.
    Rosten E., Drummond, T.: Machine learning for high-speed corner detection. In: Computer Vision ECCV, pp. 430–443 (2006)Google Scholar
  26. 26.
    Agrawal, M., Konolige, K., Blas, M.R.: Censure: center surround extremas for real-time feature detection and matching. In: European Conference on Computer Vision, pp. 102–115. Springer (2008)Google Scholar
  27. 27.
    Mair, E., Hager, G.D., Burschka, D., Suppa, M., Hirzinger, G.: Adaptive and generic corner detection based on the accelerated segment test. In: European Conference on Computer Vision, pp. 183–196. Springer, Berlin Heidelberg (2010)Google Scholar
  28. 28.
    Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2564–2571. IEEE (2011)Google Scholar
  29. 29.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)CrossRefGoogle Scholar
  30. 30.
    Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: binary robust independent elementary features. In: Computer Vision ECCV, pp. 778–792 (2010)Google Scholar
  31. 31.
    Alahi, A., Ortiz, R., Vandergheynst, P.: FREAK: fast retina keypoint. In: Computer Vision and Pattern Recognition (CVPR), pp. 510–517. IEEE (2012)Google Scholar
  32. 32.
    Hassaballah, M., Abdelmgeid, A.A., Alshazly, H.A.: Image features detection, description and matching. In: Image Feature Detectors and Descriptors Studies in Computational Intelligence, pp. 11–45. Springer (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of MalakandLower DirPakistan

Personalised recommendations