Human Gait Tracking Based on Linear Model Fitting

This paper presents a method of human gait tracking, which determines the parameters - joint angles of human motion by linear model fitting. First a 3D model of human body is designed then the joint angles are computed according to the contour corresponding to the model components. This method combines the 3D model of human body and contour information in images to get the joint angles of human body. It is more accurate and faster than the other methods of same kind. This paper gives the experimental results and the solutions to the problems met in the application

[1]  D. Lowe.  Fitting Parameterized 3-D Models to Images , 1989 .

[2]  David G. Lowe,et al.  Fitting Parameterized Three-Dimensional Models to Images , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  A. Murat Tekalp,et al.  Feature extraction for the analysis of gait and human motion , 2002, Object recognition supported by user interaction for service robots.

[4]  Soon Ki Jung,et al.  Particle filter with analytical inference for human body tracking , 2002, Workshop on Motion and Video Computing, 2002. Proceedings..

[5]  Fabio Remondino,et al.  3D Reconstruction of Human Skeleton from Single Images or Monocular Video Sequences , 2003, DAGM-Symposium.

[6]  Sangho Park,et al.  Segmentation and tracking of interacting human body parts under occlusion and shadowing , 2002, Workshop on Motion and Video Computing, 2002. Proceedings..

[7]  Tieniu Tan,et al.  Kinematics-based tracking of human walking in monocular video sequences , 2004, Image Vis. Comput..

[8]  Pascal Fua,et al.  3D Human Body Tracking Using Deterministic Temporal Motion Models , 2004, ECCV.

[9]  José M. F. Moura,et al.  Capture and Representation of Human Walking in Live Video Sequences , 1999, IEEE Trans. Multim..

[10]  Olivier D. Faugeras,et al.  3D Articulated Models and Multiview Tracking with Physical Forces , 2001, Comput. Vis. Image Underst..

[11]  Ling Guan,et al.  Quantifying and recognizing human movement patterns from monocular video Images-part I: a new framework for modeling human motion , 2004, IEEE Transactions on Circuits and Systems for Video Technology.