Abstract
In this paper, we propose a novel online multi-camera framework for person identification based on gait recognition using Grassmann Discriminant Analysis. We propose an online method wherein the gait space of individuals are created as they are tracked. The gait space is view invariant and the recognition process is carried out in a distributed manner. We assume that only a fixed known set of people are allowed to enter the area under observation. During the training phase, multi-view data of each individual is collected from each camera in the network and their global gait space is created and stored. During the test phase, as an unknown individual is observed by the network of cameras, simultaneously or sequentially, his/her gait space is created. Grassmann manifold theory is applied for classifying the individual. The gait space of an individual is a point on a Grassmann manifold and distance between two gait spaces is the same as distance between two points on a Grassmann manifold. Person identification is, therefore, carried out on-the-fly based on the uniqueness of gait, using Grassmann discriminant analysis.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Weng, J., Zhang, Y., Hwang, W.S.: Candid covariance-free incremental principal component analysis. IEEE Trans. Pattern Anal. Mach. Intell. 25(8), 1034–1040 (2003)
Hamm, J., Lee, D.D: Grassmann discriminant analysis: a unifying view on subspace-based learning. In: International Conference on Machine Learning, pp. 376–383 (2008)
Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 2246–2252 (1999)
Bobick, A.F., Johnson, A.Y.: Gait recognition using static, activity-specific parameters. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 423–430 (2001)
Jang-Hee, Y., Doosung, H., Ki-Young, M., Nixon, M.S: Automated human recognition by gait using neural network. In: First Workshop on Image Processing Theory, Tools and Applications, pp. 1–6 (2008)
Boulgouris, N.V., Chi, Z.X.: Human gait recognition based on matching of body components. Pattern Recogn. 40, 1763–1770 (2007)
Xuelong, L., Maybank, S.J., Shuicheng, Y., Dacheng, T., Dong, X.: Gait components and their application to gender recognition. IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev. 38, 145–155 (2008)
Sarkar, S., Phillips, P.J., Liu, Z., Vega, I.R., Grother, P., Bowyer, K.W.: The humanID gait challenge problem: data sets, performance, and analysis. IEEE Trans. Pattern Anal. Mach. Intell. 27, 162–177 (2005)
Bobick, A.F., Davis, J.W.: The recognition of human movement using temporal templates. IEEE Trans. Pattern Anal. Mach. Intell. 23, 257–267 (2001)
Han, J., Bhanu, B.: Individual recognition using gait energy image. IEEE Trans. Pattern Anal. Mach. Intell. 28, 316–322 (2006)
Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77(2), 257–286 (1989)
Collins, R.T., Gross, R., Jianbo, S.: Silhouette-based human identification from body shape and gait. In: Fifth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 366–371 (2002)
CASIA Gait Database. http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp
Han, J., Bhanu, B., Chowdhury, A.K.R.: A study on view-insensitive gait recognition. In: International Conference on Image Processing, pp. 297–300 (2005)
Tee, C., Goh, M.K.O., Teoh, A.B.J.: Gait recognition using sparse grassmannian locality preserving discriminant analysis. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2989–2993 (2013)
Yan, S., Xu, D., Zhang, B., Zhang, H.-J., Yang, Q., Lin, S.: Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans. Pattern Anal. Mach. Intell. 29(1), 40–51 (2007)
BenAbdelkader, C., Cutler, R., Davis, L.: Motion-based recognition of people in EigenGait space. In: Fifth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 267–272 (2002)
Hall, P., Marshall, D., Martin, R.: Merging and splitting eigenspace models. IEEE Trans. Pattern Anal. Mach. Intell. 22(9), 1042–1049 (2000)
Jean, F., Bergevin, R., Branzan, A.: Trajectories normalization for viewpoint invariant gait recognition. In: International Conference on Pattern Recognition, pp. 1–4 (2008)
Kusakunniran, W., Wu, Q., Li, H., Zhang, J.: Multiple views gait recognition using view transformation model based on optimized gait energy image. In: International Conference on Computer Vision (ICCV) Workshop, pp. 1058–1064 (2009)
Golub, G.H., Van Loan, C.F.: Matrix Computations Johns Hopkins (1983)
Makihara, Y., Sagawa, R., Mukaigawa, Y., Echigo, T., Yagi, Y.: Gait recognition using a view transformation model in the frequency domain. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3953, pp. 151–163. Springer, Heidelberg (2006)
Bashir, K., Xiang, T., Gong, S.: Cross-view gait recognition using correlation strength. In: British Machine Vision Conference, pp. 1–11 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Choudhary, A., Chaudhury, S. (2015). Gait Recognition Based Online Person Identification in a Camera Network. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9008. Springer, Cham. https://doi.org/10.1007/978-3-319-16628-5_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-16628-5_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16627-8
Online ISBN: 978-3-319-16628-5
eBook Packages: Computer ScienceComputer Science (R0)