Abstract
Both the human motion characteristics and body part measurement are important cues for human recognition at a distance. The former can be viewed as kinematic measurement while the latter is stationary measurement. In this paper, we propose a kinematic-based approach to extract both kinematic and stationary features for human recognition. The proposed approach first estimates 3D human walking parameters by fitting the 3D kinematic model to the 2D silhouette extracted from a monocular image sequence. Kinematic and stationary features are then extracted from the kinematic and stationary parameters, respectively, and used for human recognition separately. Next, we discuss different strategies for combining kinematic and stationary features to make a decision. Experimental results show a comparison of these combination strategies and demonstrate the improvement in performance for human recognition.
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References
S.A. Niyogi, E.H. Adelson. Analyzing and recognizing walking figures in XYT. in Proc. IEEE Conference on CVPR, pp. 469–474, 1994.
J. J. Little, J. E. Boyd. Recognizing people by their gait: the shape of motion. Videre: Journal of Computer Vision Research, 1(2):469–474, 1998.
H. Murase and R. Sakai. Moving object recognition in eigenspace representation: gait analysis and lip reading. Pattern Recognition Letters, 17(2):155–62, 1996.
P. S. Huang and C. J. Harris and M. S. Nixon. Recognizing humans by gait via parameteric canonical space. Artificial Intelligence in Engineering, 13:359–366, 1999.
M.H. Lin. Tracking articulated objects in real-time range image sequences. in Proc. ICCV, pp. 648–653, 1999.
S. Wachter and H.-H. Nagel. Tracking of persons in monocular image sequences. in Proc. IEEE Workshop on Nonrigid and Articulated Motion, pp. 2–9, 1997.
B. Bhanu and J. Han. Individual recognition by kinematic-based gait analysis. in Proc. International Conference on Pattern Recognition, (3):343–346, 2002.
J. Kittler, M. Hatef, R. Duin, and J. Matas. On Combining Classifiers. IEEE Trans. PAMI, 20(3):226–239, 2001.
G. Shakhnarovich and T. Darrell. On probabilistic combination of face and gait cues for identification. in Proc. IEEE International Conferenceon Automatic Face and Gesture Recognition, pp. 169–174, 2002.
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© 2003 Springer-Verlag Berlin Heidelberg
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Bhanu, B., Han, J. (2003). Human Recognition on Combining Kinematic and Stationary Features. In: Kittler, J., Nixon, M.S. (eds) Audio- and Video-Based Biometric Person Authentication. AVBPA 2003. Lecture Notes in Computer Science, vol 2688. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44887-X_71
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DOI: https://doi.org/10.1007/3-540-44887-X_71
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