Synonyms
Appearance-based gait analysis; Silhouette analysis for gait recognition
Definition
The appearance of gait in an image sequence is a spatiotemporal process that characterizes the walker. The spatiotemporal characteristics of gait contain rich perceptual information about the body configuration, the person’s gender, the person’s identity, and even the emotional states of the person. Motion analysis for gait recognition is a computer vision task that aims to capture discriminative spatiotemporal features (signature) from image sequences in order to achieve human identification. Such a signature ought to be invariant to the presence of various viewing conditions, such as viewpoint, people clothing, etc. In contrast to model-based gait analysis systems, which is another article, the goal here is to capture gait characteristics without fitting a body model or locating the body limbs, rather by analyzing the feature distribution over the space and time extent of the motion.
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References
D. Cunado, M.S. Nixon, J. Carter, Automatic extraction and description of human gait models for recognition purposes. Compt. Vis. Image Underst. 90, 1–41 (2003)
S. Sarkar, P.J. Phillips, Z. Liu, I.R. Vega, P. Grother, K.W. Bowyer, The humanid gait challenge problem: data sets, performance, and analysis. IEEE Trans. PAMI 27(2), 162–177 (2005)
G. Johansson, Visual motion perception. Sci. Am. 232, 76–88 (1975)
J.E. Cutting, L.T. Kozlowski, Recognizing friends by their walk: gait perception without familiarity cues. Bull. Psychon. Soc. 9(5), 353–356 (1977)
D.M. Gavrila, The visual analysis of human movement: a survey. Comput. Vis. Image Underst. 73(1), 82–98 (1999). doi:http://dx.doi.org/10.1006/cviu.1998.0716
T.B. Moeslund, A. Hilton, V. Krüger, A survey of advances in vision-based human motion capture and analysis. Comput. Vis. Image Underst. 104(2), 90–126 (2006). doi:http://dx.doi.org/10.1016/j.cviu.2006.08.002
S.E. Palmer, Vision Science, Photons to Phenomenology (MIT, Cambridge, 1999)
C. BenAdbelkader, R. Cutler, L. Davis, Motion-based recognition of people using image self-similarity, in Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition, Washington, DC, 2002, pp. 254–259
S. Niyogi, E. Adelson, Analyzing and recognition walking figures in xyt, in Proceedings of IEEE CVPR, Seattle, 1994, pp. 469–474
H. Murase, R. Sakai Moving object recognition in eigenspace representation: gait analysisand lip reading. Pattern Recogn. Lett. 17, 155–162 (1996)
P. Huang, C. Haris, M. Nixon, Recogising humans by gait via parametric canonical space. Artif. Intell. Eng. 13, 359–366 (1999)
J.J. Little, J.E. Boyd, Recognizing people by their gait: the shape of motion. Videre J. Comput. Vis. Res. 1(2), 2–32 (1998)
J.B. Hayfron-Aquah, M.S. Nixon, J.N. Carter, Automatic gait recognition by symmetry analysis. Pattern Recogn. Lett. 24, 2175–2183 (2003)
Q. He, C. Debrunner, Individual recognition from periodic activity using hidden markov models, in Proceedings of the Workshop on Human Motion. (Humo) (Dec 2007), HUMO, Rio de Janeiro. IEEE Computer Society, Washington, DC
A. Kale, A. Sundaresan, A.N. Rajagopalan, N.P. Cuntoor, A.K. Roy-Chowdhury, V. Kruger, R. Chellappa, Identification of human using gait. IEEE Trans. Image Process. 13(9), 1163–1173 (2004);
C.S. Lee, Elgammal, A.: Gait style and gait content: bilinear model for gait recogntion using gait re-sampling, in Proceedings of FGR, Seoul, 2004, pp. 147–152
A. Elgammal, C.S. Lee, Inferring 3d body pose from silhouettes using activity manifold learning, in Proceeedings of CVPR, Washington, DC, vol. 2, 2004, pp. 681–688
R. Urtasun, D.J. Fleet, A. Hertzmann, Fua, P.: Priors for people tracking from small training sets, in ICCV , Beijing, 2005, pp. 403–410
S. Roweis, L. Saul, Nonlinear dimensionality reduction by locally linar embedding. Science 290(5500), 2323–2326 (2000)
J. Tenenbaum, Mapping a manifold of perceptual observations, in Proceedings of Advances in Neural Information Processing (NIPS), Denver, vol. 10, 1998, pp. 682–688
N.D. Lawrence, Gaussian process models for visualisation of high dimensional data, in Proceedings of NIPS. BMVC, Oxford, 2004
C.S. Lee, A. Elgammal, Style adaptive bayesian tracking using explicit manifold learning, in Proceedings of British Machine Vision Conference, Oxford, 2005
C.S. Lee, A. Elgammal, Audio- and Video-Based Biometric Person Authentication Conference AVBPA, Terrytown, 2005
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Elgammal, A. (2015). Gait Recognition, Motion Analysis for. In: Li, S.Z., Jain, A.K. (eds) Encyclopedia of Biometrics. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7488-4_40
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DOI: https://doi.org/10.1007/978-1-4899-7488-4_40
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