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FSSGR: Feature Selection System to Dynamic Gesture Recognition

  • Diego G. S. Santos
  • Rodrigo C. Neto
  • Bruno Fernandes
  • Byron Bezerra
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9281)

Abstract

Dynamic gesture recognition systems based on computer vision techniques have been frequently used in some fields such as medical, games and sign language. Usually, these systems have a time execution problem caused by the elevated number of features or attributes extracted for gesture classification. This work presents a system for dynamic gesture recognition that uses Particle Swarm Optimization to reduce the feature vector while increases the classification capability. The system FSSGR, Feature Selection System to Dynamic Gesture Recognition, solved the gesture recognition problem in RPPDI dataset, achieving 99.21% of classification rate with the same vectors size of previous works on the same database, although with a better response time.

Keywords

Particle Swarm Optimization Feature Vector Gesture Recognition Smart City Hand Gesture Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Yang, J., Wang, Y., Sowmya, A., Li, Z.: Vehicle detection and tracking with low-angle cameras. In: ICIP, pp. 685–688 (2010)Google Scholar
  2. 2.
    Then, Y.B., Tay, Y.H., Ho, W.T.: Estimating traffic condition using just a single image. In: ICIP, pp. 3331–3335 (2013)Google Scholar
  3. 3.
    Yang, M., Zhang, L.: Gabor feature based sparse representation for face recognition with gabor occlusion dictionary. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part VI. LNCS, vol. 6316, pp. 448–461. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  4. 4.
    Calinon, S., Billard, A.: Recognition and reproduction of gestures using a probabilistic framework combining pca, ica and hmm. In: Proceedings of the 22nd International Conference on Machine Learning, pp. 105–112. ACM (2005)Google Scholar
  5. 5.
    Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: 1997 IEEE International Conference on Systems, Man, and Cybernetics, Computational Cybernetics and Simulation, vol. 5, pp. 4104–4108. IEEE (1997)Google Scholar
  6. 6.
    Fernandes, B.J.T., Santos, D.G.S., Neto, R.C., Bezerra, B.L.D.: A dynamic gesture recognition system based on cipbr algorithm. In: Computer Vision, 2014 Mexican International Conference on Artificial Intelligence. LNAI (2014)Google Scholar
  7. 7.
    Barros, P.V., Junior, N., Bisneto, J.M., Fernandes, B.J., Bezerra B.L., Fernandes, S.M.: Convexity local contour sequences for gesture recognition. In: Proceedings of the 28th Annual ACM Symposium on Applied Computing, pp. 34–39. ACM (2013)Google Scholar
  8. 8.
    Keogh, E., Wei, L., Xi, X., Lee, S.-H., Vlachos, M.: Lb_keogh supports exact indexing of shapes under rotation invariance with arbitrary representations and distance measures. In: Proceedings of the 32nd International Conference on Very large Data Bases, pp. 882–893. VLDB Endowment (2006)Google Scholar
  9. 9.
    Day, A.: Planar convex hull algorithms in theory and practice. Computer Graphics Forum 7(3), 177–193 (1988)Google Scholar
  10. 10.
    Eberhart, R.C., Shi, Y.: Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the 2000 Congress on Evolutionary Computation, vol. 1, pp. 84–88. IEEE (2000)Google Scholar
  11. 11.
    Hartigan, J.A., Wong, M.A.: Algorithm as 136: A k-means clustering algorithm. Applied statistics, pp. 100–108 (1979)Google Scholar
  12. 12.
    Normandin, Y.: Hidden markov models. Automatic Speech and Speaker Recognition: Advanced Topics 355, 57 (2012)CrossRefGoogle Scholar
  13. 13.
    Julka, A., Bhargava, S.: A static hand gesture recognition based on local contour sequence. International Journal of Advanced Research in Computer Science and Software Engineerin 3(7), 918–924 (2013)Google Scholar
  14. 14.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  15. 15.
    Wang, J., Liu, Z., Chorowski, J., Chen, Z., Wu, Y.: Robust 3D action recognition with random occupancy patterns. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 872–885. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  16. 16.
    Klaser, A., Marszałek, M., Schmid, C.: A spatio-temporal descriptor based on 3d-gradients. In: BMVC 2008–19th British Machine Vision Conference, p. 275-1. British Machine Vision Association (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Diego G. S. Santos
    • 1
  • Rodrigo C. Neto
    • 1
  • Bruno Fernandes
    • 1
  • Byron Bezerra
    • 1
  1. 1.Escola Politécnica de Pernambuco - Universidade de PernambucoRecifeBrazil

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