Gait Recognition Using Motion Trajectory Analysis

  • Muhammad Hassan Khan
  • Frederic Li
  • Muhammad Shahid Farid
  • Marcin Grzegorzek
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 578)


Gait recognition has received significant attention in the recent years due to its applications in numerous fields of computer vision, particularly in automated person identification in visual surveillance and monitoring systems. In this paper, we propose a novel algorithm for gait recognition using spatio-temporal motion characteristics of a person. The proposed algorithm consists of four steps. First, motion features are extracted from video sequence which are used to generate a codebook in the second step. In a third step, the local descriptors are encoded using Fisher vector encoding. Finally, the encoded features are classified using linear Support Vector Machine (SVM). The performance of the proposed algorithm is evaluated and compared with state-of-the-art on two widely used gait databases TUM GAID and CASIA-A. The recognition results demonstrate the effectiveness of the proposed algorithm.


Gait recognition Spatiotemporal model Fisher vector encoding Visual surveillance 


  1. 1.
    Bashir, K., Xiang, T., Gong, S.: Gait recognition without subject cooperation. Pattern Recognit. Lett. 31(13), 2052–2060 (2010)CrossRefGoogle Scholar
  2. 2.
    Bashir, K., Xiang, T., Gong, S., Mary, Q.: Gait representation using flow fields. In: BMVC, pp. 1–11 (2009)Google Scholar
  3. 3.
    Bay, H., Tuytelaars, T., Van Gool, L.: Surf: speeded up robust features. In: ECCV 2006, pp. 404–417 (2006)Google Scholar
  4. 4.
    Bouchrika, I., Nixon, M.S.: Model-based feature extraction for gait analysis and recognition. In: Gagalowicz, A., Philips, W. (eds.) MIRAGE 2007. LNCS, vol. 4418, pp. 150–160. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-71457-6_14 CrossRefGoogle Scholar
  5. 5.
    Chai, Y., Wang, Q., Jia, J., Zhao, R.: A novel human gait recognition method by segmenting and extracting the region variance feature. IEEE ICPR 4, 425–428 (2006)Google Scholar
  6. 6.
    Chen, C., Liang, J., Zhao, H., Hu, H., Tian, J.: Frame difference energy image for gait recognition with incomplete silhouettes. Pattern Recognit. Lett. 30(11), 977–984 (2009)CrossRefGoogle Scholar
  7. 7.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE CVPR, vol. 1, pp. 886–893, June 2005Google Scholar
  8. 8.
    Dalal, N., Triggs, B., Schmid, C.: Human detection using oriented histograms of flow and appearance. In: ECCV, pp. 428–441 (2006)Google Scholar
  9. 9.
    Fan, R.E., et al.: LIBLINEAR: a library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008)MATHGoogle Scholar
  10. 10.
    Gianaria, E., Balossino, N., Grangetto, M., Lucenteforte, M.: Gait characterization using dynamic skeleton acquisition. In: Proceedings of International Workshop Multimedia Signal Process, (MMSP), pp. 440–445, September 2013Google Scholar
  11. 11.
    Hofmann, M., Bachmann, S., Rigoll, G.: 2.5D gait biometrics using the depth gradient histogram energy image. In: IEEE BTAS, pp. 399–403 (2012)Google Scholar
  12. 12.
    Khan, M.H., Helsper, J., Yang, C., Grzegorzek, M.: An automatic vision-based monitoring system for accurate Vojta-therapy. In: IEEE/ACIS ICIS, pp. 1–6 (2016)Google Scholar
  13. 13.
    Khan, M.H., Helsper, J., Boukhers, Z., Grzegorzek, M.: Automatic recognition of movement patterns in the Vojta-therapy using RGB-D data. In: IEEE ICIP, pp. 1235–1239 (2016)Google Scholar
  14. 14.
    Khan, M.H., Shirahama, K., Farid, M.S., Grzegorzek, M.: Multiple human detection in depth images. In: IEEE International Workshop on MMSP, pp. 1–6 (2016)Google Scholar
  15. 15.
    Kusakunniran, W.: Attribute-based learning for gait recognition using spatio-temporal interest points. Image Vis. Comput. 32(12), 1117–1126 (2014)CrossRefGoogle Scholar
  16. 16.
    Lam, T.H., Cheung, K.H., Liu, J.N.: Gait flow image: a silhouette-based gait representation for human identification. Pattern Recognit. 44(4), 973–987 (2011)CrossRefMATHGoogle Scholar
  17. 17.
    Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: IEEE CVPR, pp. 1–8, June 2008Google Scholar
  18. 18.
    Little, J., Boyd, J.: Recognizing people by their gait: the shape of motion. Videre J. Comput. Vis. Res. 1(2), 1–32 (1998)Google Scholar
  19. 19.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: EEE ICCV, vol. 2, pp. 1150–1157 (1999)Google Scholar
  20. 20.
    Man, J., Bhanu, B.: Individual recognition using gait energy image. IEEE Trans. Pattern Anal. Mach. Intell. 28(2), 316–322 (2006)CrossRefGoogle Scholar
  21. 21.
    McLachlan, G., Peel, D.: Finite Mixture Models. Wiley, New York (2004)MATHGoogle Scholar
  22. 22.
    Nixon, M.S., Tan, T., Chellappa, R.: Human Identification Based on Gait. Springer, Heidelberg (2010)Google Scholar
  23. 23.
    Peng, X., Wang, L., Wang, X., Qiao, Y.: Bag of visual words and fusion methods for action recognition: comprehensive study and good practice. Comput. Vis. Image Underst. 150, 109–125 (2016). CrossRefGoogle Scholar
  24. 24.
    Peng, X., Zou, C., Qiao, Y., Peng, Q.: Action recognition with stacked fisher vectors. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, pp. 581–595. Springer, Heidelberg (2014)Google Scholar
  25. 25.
    Perronnin, F., Sánchez, J., Mensink, T.: Improving the Fisher Kernel for large-scale image classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 143–156. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-15561-1_11 CrossRefGoogle Scholar
  26. 26.
    Sanchez, J., Perronnin, F., Mensink, T., Verbeek, J.: Image classification with the fisher vector: theory and practice. Int. J. Comput. Vis. 105(3), 222–245 (2013)MathSciNetCrossRefMATHGoogle Scholar
  27. 27.
    Sivapalan, S., Chen, D., Denman, S., Sridharan, S., Fookes, C.: Gait energy volumes and frontal gait recognition using depth images. In: IEEE IJCB, pp. 1–6 (2011)Google Scholar
  28. 28.
    Sivapalan, S., Chen, D., Denman, S., Sridharan, S., Fookes, C.: Histogram of weighted local directions for gait recognition. In: IEEE CVPR Workshop, pp. 125–130 (2013)Google Scholar
  29. 29.
    Sun, C., Nevatia, R.: Large-scale web video event classification by use of fisher vectors. In: IEEE WACV, pp. 15–22 (2013)Google Scholar
  30. 30.
    Wang, C., Zhang, J., Wang, L., Pu, J., Yuan, X.: Human identification using temporal information preserving gait template. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2164–2176 (2012)CrossRefGoogle Scholar
  31. 31.
    Wang, H., Schmid, C.: Action recognition with improved trajectories. In: IEEE ICCV, pp. 3551–3558 (2013)Google Scholar
  32. 32.
    Wang, L., Ning, H., Tan, T., Hu, W.: Fusion of static and dynamic body biometrics for gait recognition. IEEE Trans. Circuits Syst. Video Technol. 14(2), 149–158 (2004)CrossRefGoogle Scholar
  33. 33.
    Wang, L., Tan, T., Hu, W., Ning, H.: Automatic gait recognition based on statistical shape analysis. IEEE Trans. Image Process. 12(9), 1120–1131 (2003)MathSciNetCrossRefGoogle Scholar
  34. 34.
    Wang, L., Tan, T., Ning, H., Hu, W.: Silhouette analysis-based gait recognition for human identification. IEEE Trans. Pattern Anal. Mach. Intell. 25(12), 1505–1518 (2003)CrossRefGoogle Scholar
  35. 35.
    Whytock, T., Belyaev, A., Robertson, N.M.: Dynamic distance-based shape features for gait recognition. J. Math. Imaging Vis. 50(3), 314–326 (2014)CrossRefMATHGoogle Scholar
  36. 36.
    Yang, Y., Tu, D., Li, G.: Gait recognition using flow histogram energy image. In: IEEE ICPR, pp. 444–449. IEEE (2014)Google Scholar
  37. 37.
    Zeng, W., Wang, C., Yang, F.: Silhouette-based gait recognition via deterministic learning. Pattern Recognit. 47(11), 3568–3584 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Muhammad Hassan Khan
    • 1
  • Frederic Li
    • 1
  • Muhammad Shahid Farid
    • 2
  • Marcin Grzegorzek
    • 3
  1. 1.Research Group of Pattern RecognitionUniversity of SiegenSiegenGermany
  2. 2.College of Information TechnologyUniversity of the PunjabLahorePakistan
  3. 3.Faculty of Informatics and CommunicationUniversity of Economics in KatowiceKatowicePoland

Personalised recommendations