Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 563864, 35 pages
Research Article

Automatic Human Gait Imitation and Recognition in 3D from Monocular Video with an Uncalibrated Camera

Tao Yu1,2 and Jian-Hua Zou1,2

1Systems Engineering Institute, School of Electronic & Information Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China
2State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China

Received 26 September 2011; Accepted 16 December 2011

Academic Editor: Yun-Gang Liu

Copyright © 2012 Tao Yu and Jian-Hua Zou. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A framework of imitating real human gait in 3D from monocular video of an uncalibrated camera directly and automatically is proposed. It firstly combines polygon-approximation with deformable template-matching, using knowledge of human anatomy to achieve the characteristics including static and dynamic parameters of real human gait. Then, these characteristics are processed in regularization and normalization. Finally, they are imposed on a 3D human motion model with prior constrains and universal gait knowledge to realize imitating human gait. In recognition based on this human gait imitation, firstly, the dimensionality of time-sequences corresponding to motion curves is reduced by NPE. Then, we use the essential features acquired from human gait imitation as input and integrate HCRF with SVM as a whole classifier, realizing identification recognition on human gait. In associated experiment, this imitation framework is robust for the object’s clothes and backpacks to a certain extent. It does not need any manual assist and any camera model information. And it is fitting for straight indoors and the viewing angle for target is between 60° and 120°. In recognition testing, this kind of integrated classifier HCRF/SVM has comparatively higher recognition rate than the sole HCRF, SVM and typical baseline method.