Title :
Gait tracking and recognition using person-dependent dynamic shape model
Author :
Lee, Chan-Su ; Elgammal, Ahmed
Author_Institution :
Dept. of Comput. Sci., Rutgers Univ., Piscataway, NJ
Abstract :
The characteristics of the 2D shape deformation in human motion contain rich information for human identification and pose estimation. In this paper, we introduce a framework for simultaneous gait tracking and recognition using person-dependent global shape deformation model. Person-dependent global shape deformations are modeled using a nonlinear generative model with kinematic manifold embedding and kernel mapping. The kinematic manifold is used as a common representation of body pose dynamics in different people in a low dimensional space. Shape style as well as geometric transformation and body pose are estimated within a Bayesian framework using the generative model of global shape deformation. Experimental results show person-dependent synthesis of global shape deformation, gait recognition from extracted silhouettes using style parameters, and simultaneous gait tracking and recognition from image edges
Keywords :
Bayes methods; edge detection; feature extraction; gait analysis; gesture recognition; image motion analysis; 2D shape deformation; Bayesian framework; body pose dynamics; gait recognition; gait tracking; human identification; human motion; image edges; kinematic manifold; nonlinear generative model; person-dependent global shape deformation model; person-dependent synthesis; pose estimation; silhouettes extraction; Bayesian methods; Biological system modeling; Deformable models; Humans; Image recognition; Kernel; Kinematics; Motion estimation; Shape; Solid modeling;
Conference_Titel :
Automatic Face and Gesture Recognition, 2006. FGR 2006. 7th International Conference on
Conference_Location :
Southampton
Print_ISBN :
0-7695-2503-2
DOI :
10.1109/FGR.2006.58
