CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Tracking and Recognizing Two-Person Interactions in Outdoor Image Sequences
WOMOT '01 Proceedings of the IEEE Workshop on Multi-Object Tracking (WOMOT'01)
Articulated Body Motion Capture by Stochastic Search
International Journal of Computer Vision
On Modelling Nonlinear Shape-and-Texture Appearance Manifolds
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
3D People Tracking with Gaussian Process Dynamical Models
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A Quantitative Evaluation of Video-based 3D Person Tracking
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Inferring 3D body pose from silhouettes using activity manifold learning
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Human action recognition by feature-reduced Gaussian process classification
Pattern Recognition Letters
Charting-based subspace learning for video-based human action classification
Machine Vision and Applications
Hi-index | 0.00 |
This paper presents a framework for 3D articulated human body tracking and action classification. The method is based on nonlinear dimensionality reduction of high dimensional data space to low dimensional latent space. Motion of human body is described by concatenation of low dimensional manifolds which characterize different motion types. We introduce a body pose tracker, which uses the learned mapping function from low dimensional latent space to high dimensional body pose space. The trajectories in the latent space provide low dimensional representations of body poses performed during motion. They are used to classify human actions. The approach was checked on HumanEva dataset as well as on our own one. The results and the comparison to other methods are presented.