Semantic Attribute Classification Related to Gait

  • Imen Chtourou
  • Emna Fendri
  • Mohamed Hammami
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 736)


Human gait, as a behavioral biometric, has recently gained significant attention from computer vision researchers. But there are some challenges which hamper using this biometric in real applications. Among these challenges is clothing variations and carrying objects which influence on its accuracy. In this paper, we propose a semantic classification based method in order to deal with such challenges. Different predictive models are elaborated in order to determine the most relevant model for this task. Experimental results on CASIA-B gait database show the performance of our proposed method.


Pedestrian analysis Semantic attributes Classification 


  1. 1.
    Gray, D., Brennan, S., Tao, H.: Evaluating appearance models for recognition, reacquisition, and tracking. In: Proceedings of the IEEE International Workshop on Performance Evaluation for Tracking and Surveillance (PETS), vol. 3, no. 5, pp. 1–7. Citeseer, October 2007Google Scholar
  2. 2.
    Hirzer, M., Beleznai, C., Roth, P.M., Bischof, H.: Person re-identification by descriptive and discriminative classification. In: Scandinavian Conference on Image Analysis, pp. 91–102. Springer, Heidelberg, May 2011Google Scholar
  3. 3.
    Liu, S., Song, Z., Liu, G., Xu, C., Lu, H., Yan, S.: Street-to-shop: cross-scenario clothing retrieval via parts alignment and auxiliary set. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3330–3337. IEEE, June 2012Google Scholar
  4. 4.
    Layne, R., Hospedales, T.M., Gong, S.: Attributes-based re-identification. In: Person Re-identification, pp. 93–117. Springer, London (2014)Google Scholar
  5. 5.
    SchIlkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2002)Google Scholar
  6. 6.
    Bashir, K., Xiang, T., Gong, S.: Gait recognition without subject cooperation. Pattern Recognit. Lett. 31(13), 2052–2060 (2010)CrossRefGoogle Scholar
  7. 7.
    Vaquero, D.A., Feris, R.S., Tran, D., Brown, L., Hampapur, A., Turk, M.: Attribute-based people search in surveillance environments. In: Workshop on Applications of Computer Vision (WACV), pp. 1–8. IEEE, December 2009Google Scholar
  8. 8.
    Bourdev, L., Maji, S., Malik, J.: Describing people: a poselet-based approach to attribute classification. In: IEEE International Conference on Computer Vision (ICCV), pp. 1543–1550. IEEE, November 2011Google Scholar
  9. 9.
    Bourdev, L., Malik, J.: Poselets: body part detectors trained using 3D human pose annotations. In: IEEE 12th International Conference on Computer Vision, pp. 1365–1372. IEEE, September 2009Google Scholar
  10. 10.
    Chen, H., Gallagher, A., Girod, B.: Describing clothing by semantic attributes. In: Computer Vision – ECCV 2012, pp. 609–623 (2012)Google Scholar
  11. 11.
    Yang, M., Yu, K.: Real-time clothing recognition in surveillance videos. In: 18th IEEE International Conference on Image Processing (ICIP), pp. 2937–2940. IEEE, September 2011Google Scholar
  12. 12.
    Nguyen, N.B., Nguyen, V.H., Duc, T.N., Duong, D.A.: Using attribute relationships for person re-identification. In: Knowledge and Systems Engineering, pp. 195–207. Springer, Cham (2015)Google Scholar
  13. 13.
    Umeda, T., Sun, Y., Irie, G., Sudo, K., Kinebuchi, T.: Attribute discovery for person re-identification. In: International Conference on Multimedia Modeling, pp. 268–276. Springer, Cham, January 2016Google Scholar
  14. 14.
    Yegnanarayana, B.: Artificial Neural Networks. PHI Learning Pvt. Ltd., New Delhi (2009)Google Scholar
  15. 15.
    Lin, Y., Zheng, L., Zheng, Z., Wu, Y., Yang, Y.: Improving person re-identification by attribute and identity learning. arXiv preprint arXiv:1703.07220 (2017)
  16. 16.
    Li, D., Chen, X., Huang, K.: Multi-attribute learning for pedestrian attribute recognition in surveillance scenarios. In: 3rd IAPR Asian Conference on Pattern Recognition (ACPR), pp. 111–115. IEEE, November 2015Google Scholar
  17. 17.
    Zhu, J., Liao, S., Yi, D., Lei, Z., Li, S.Z.: Multi-label CNN based pedestrian attribute learning for soft biometrics. In: International Conference on Biometrics (ICB), pp. 535–540. IEEE, May 2015Google Scholar
  18. 18.
    Matsukawa, T., Suzuki, E.: Person re-identification using CNN features learned from combination of attributes. In: 23rd International Conference on Pattern Recognition (ICPR), pp. 2428–2433. IEEE, December 2016Google Scholar
  19. 19.
    Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: The KDD process for extracting useful knowledge from volumes of data. Commun. ACM 39(11), 27–34 (1996)CrossRefGoogle Scholar
  20. 20.
    Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 144–152. ACM, July 1992Google Scholar
  21. 21.
    Zighed, D.A., Rakotomalala, R.: Graphes d’induction: apprentissage et data mining. Hermes, Paris (2000)Google Scholar
  22. 22.
    Zhu, J., Liao, S., Lei, Z., Li, S.Z.: Multi-label convolutional neural network based pedestrian attribute classification. Image Vis. Comput. 58, 224–229 (2017)CrossRefGoogle Scholar
  23. 23.
    Zheng, S.: CASIA gait database (2005).
  24. 24.
    Shih, F.Y.: Image Processing and Pattern Recognition: Fundamentals and Techniques. Wiley, Hoboken (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.MIRACL Laboratory, ENISUniversity of SfaxSfaxTunisia
  2. 2.MIRACL Laboratory, FSSUniversity of SfaxSfaxTunisia

Personalised recommendations