Speed Invariant, Human Gait Based Recognition System for Video Surveillance Security

  • Priydarshi
  • Anup Nandy
  • Pavan Chakraborty
  • G. C. Nandi
Part of the Communications in Computer and Information Science book series (CCIS, volume 276)


Human gait provides an important and useful behavioral biometric signature which characterizes the nature of an individual’s walking pattern. This inherent knowledge of gait feature confirms the correct identification of a person in a video surveillance footage scenario. In this paper, we attempt to use computer vision based technique to derive the gait signature of a person which is a major criterion for the gait based recognition system. The gait signature has been obtained from the sequence of silhouette images at various gait speeds varying from 2km/hr. to 7km/hr. The OU- ISIR Treadmill walking speed databases have been used in our research work. The joint angles of knee and ankle are computed from the stick figure of corresponding human silhouettes which lead to construct our feature template together with the other gait attributes such as width, height, area and diagonal angle of human silhouette. The combined gait features will make the system robust in different gait speeds. The major concept behind making the gait recognition speed invariant is that the human can walk in finite speed so instead of training the classifier for a single speed the classifier is to be trained for multiple speeds. A minimum distance classifier is used to separate out different cluster of subject with combined feature vectors at different gait speeds.


Human Gait Minimum Distance Gait Cycle Speed Invariant 


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  1. 1.
    Wang, D.K., Nixon, M.S.: On Automated Model-Based Extraction and Analysis of Gait. In: Proceeding of the IEEE Int. Conf. Face and Gesture Recognition, vol. 04 (2004)Google Scholar
  2. 2.
    Yam, C.-Y., Nixon, M.S., Carter, J.N.: Automated Person Recognition by Walking and Running via Model-Based Approaches. Pattern Recognition 37(5), 1057–1072 (2004)CrossRefGoogle Scholar
  3. 3.
    Sundaresan, A., Roy Chowdhury, A., Chellappa, R.: A Hidden Markov Model Based Framework for Recognition of Humans from Gait Sequences. In: Proceedings IEEE Int. Conf. on Image Processing, pp. 143–150 (2003)Google Scholar
  4. 4.
    Kale, A., Rajagopalan, A.N., Sundaresan, A., Cuntoor, N., Roy Chowdhury, A., Kruger, V., Chellappa, R.: Identification of Humans using Gait. IEEE Transactions on Image Processing, 1163–1173 (September 2004)Google Scholar
  5. 5.
    Sarkar, S., Phillips, P.J., Liu, Z., Vega, I.R., Grother, P., Bowyer, K.: The HumanID Gait Challenge Problem: Data Sets, Performance and Analysis. IEEE Trans on PAMI 27(2), 162–177 (2005)CrossRefGoogle Scholar
  6. 6.
    Lee, L., Grimson, W.E.L.: Gait Analysis for Recognition and Classification. In: Proceedings of the IEEE Int. Conf. Face and Gesture Recognition, pp. 155–162 (2002)Google Scholar
  7. 7.
    Bhanu, B., Han, J.: Human Recognition on Combining Kinematic and Stationary Features. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 600–608. Springer, Heidelberg (2003)Google Scholar
  8. 8.
    Yoo, J.-H., Nixon, M.S., Harris, C.J.: Extracting Human Gait Signatures by Body Segment PropertiesGoogle Scholar
  9. 9.
  10. 10.
    Guillen, E., Padilla, D., Hernandez, A., Barner, K.: Gait Recognition System: Bundle Rectangle ApproachGoogle Scholar
  11. 11.
  12. 12.
  13. 13.
    Chen, C., Liang, J., Zhu, X.: Gait recognition based on improved dynamic Bayesian networks. Elsevier Ltd. (2010)Google Scholar
  14. 14.
    Bouwmans, T., El Baf, F., Vachon, B.: Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey. Recent Patents on Computer Science 1, 3 219-237 (2008)Google Scholar
  15. 15.
    Au, O.K.-C., Tai, C.-L., Chu, H.-K., Cohen-Or, D., Lee, T.-Y.: Skeleton Extraction by Mesh Contraction. ACM (2008)Google Scholar
  16. 16.
    Sasivarnan, C., Jagan, A., Kaur, J., Jyoti, D., Rao, D.S.: Gait Recognition Using Extracted Feature Vectors. IJCST 2(3) (2011)Google Scholar
  17. 17.
    Kewatkar, S., Kathle, S.: Human Gait Recognition byOpenCV. International Journal of Computational Biology 3(1), 35–37 (2012) ISSN: 2229-6700 & E-ISSN: 2229-6719Google Scholar
  18. 18.
    Yoo, J.-H., Hwang, D., Moon, K.-Y., Nixon, M.S.: Automated Human Recognition by Gait using Neural Network. IEEE (2008)Google Scholar
  19. 19.

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Priydarshi
    • 1
  • Anup Nandy
    • 1
  • Pavan Chakraborty
    • 1
  • G. C. Nandi
    • 1
  1. 1.Robotics & AI Dept.Indian Institute of Information TechnologyAllahabadIndia

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