Recognition of Non-pedestrian Human Forms Through Locally Weighted Descriptors

  • Nancy Arana-DanielEmail author
  • Isabel Cibrian-Decena
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9423)


To recognize human forms in non-pedestrian poses presents a high complexity problem due mainly to the large number of degrees of freedom of the human body and its limbs. In this paper it is proposed a methodology to build and classify descriptors of non-pedestrian human body forms in images which is formed with local and global information. Local information is obtained by computing Local Binary Pattern (LBP) of key-body parts (head-shoulders, hands, feet, crotch-hips) detected in the image in a first stage of the method, and then this data is coupled in the descriptor with global information about euclidean distances computed between the key-body parts recognized in the image. The descriptor is then classified using a Support Vector Machine. The results obtained using the proposed recognition methodology show that it is robust to partial occlusion of bodies, furthermore the values of sensitivity, accuracy and specificity of the classifier are high enough compared with those obtained using other state of the art descriptors.


Human detection Non-pedestrian pose Local Binary Pattern (LBP) Support Vector Machine (SVM) 


  1. 1.
    Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: Application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(12), 2037–2041 (2006)CrossRefGoogle Scholar
  2. 2.
    Alanis, A.Y., Ornelas-Tellez, F., Sanchez, E.N.: Discrete-time inverse optimal neural control for synchronous generators. Eng. Appl. Artif. Intell. 26(2), 697–705 (2013)CrossRefGoogle Scholar
  3. 3.
    Andriluka, M., Pishchulin, L., Gehler, P., Schiele, B.: 2d human pose estimation: new benchmark and state of the art analysis. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2014Google Scholar
  4. 4.
    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
  5. 5.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893. IEEE (2005)Google Scholar
  6. 6.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Pictorial structures for object recognition. International Journal of Computer Vision 61(1), 55–79 (2005)CrossRefGoogle Scholar
  7. 7.
    Hanavan Jr., E.P.: A mathematical model of the human body. Technical report, DTIC Document (1964)Google Scholar
  8. 8.
    Hernandez-Gonzalez, M., Alanis, A.Y., Hernandez-Vargas, E.A.: Decentralized discrete-time neural control for a quanser 2-dof helicopter. Applied Soft Computing 12(8), 2462–2469 (2012)CrossRefGoogle Scholar
  9. 9.
    Ioffe, S., Forsyth, D.A.: Probabilistic methods for finding people. International Journal of Computer Vision 43(1), 45–68 (2001)CrossRefzbMATHGoogle Scholar
  10. 10.
    Lin, Z., Davis, L.S.: A pose-invariant descriptor for human detection and segmentation. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 423–436. Springer, Heidelberg (2008) CrossRefGoogle Scholar
  11. 11.
    López-Franco, C., Arana-Daniel, N.: A geometric algebra model for the image formation process of paracatadioptric cameras. Journal of Mathematical Imaging and Vision 43(3), 214–226 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    López-Franco, C., Arana-Daniel, N., Alanis, A.Y.: Visual servoing on the sphere using conformal geometric algebra. Advances in Applied Clifford Algebras 23(1), 125–141 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Mikolajczyk, K., Schmid, C., Zisserman, A.: Human detection based on a probabilistic assembly of robust part detectors. In: Pajdla, T., Matas, J.G. (eds.) ECCV 2004. LNCS, vol. 3021, pp. 69–82. Springer, Heidelberg (2004) CrossRefGoogle Scholar
  14. 14.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)CrossRefGoogle Scholar
  15. 15.
    Oren, M., Papageorgiou, C., Sinha, P., Osuna, E., Poggio, T.: Pedestrian detection using wavelet templates. In: Proceedings of the 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 193–199. IEEE (1997)Google Scholar
  16. 16.
    Tani, Y., Hotta, K.: Robust human detection to pose and occlusion using bag-of-words. In: 2014 22nd International Conference on Pattern Recognition (ICPR), pp. 4376–4381. IEEE (2014)Google Scholar
  17. 17.
    Vapnik, V.: The nature of statistical learning theory. Springer Science & Business Media (2000)Google Scholar
  18. 18.
    Wang, X., Han, T.X., Yan, S.: An hog-lbp human detector with partial occlusion handling. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 32–39. IEEE (2009)Google Scholar
  19. 19.
    Xia, L., Chen, C.-C., Aggarwal, J.K.: Human detection using depth information by kinect. In: 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 15–22. IEEE (2011)Google Scholar
  20. 20.
    Zhao, Y., Jia, W., Rong-Xiang, H., Min, H.: Completed robust local binary pattern for texture classification. Neurocomputing 106, 68–76 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Universidad de Guadalajara, Centro Universitario de Ciencias Exactas e IngenieríasGuadalajaraMexico

Personalised recommendations