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
Multiple view human articulated tracking using charting and particle swarm optimisation
Proceedings of the 1st international workshop on 3D video processing
Pedestrian gait classification based on Hidden Markov models
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I
Hi-index | 0.00 |
We present an approach for tracking human body parts and classification of human actions. We introduce Gaussian Processing Annealed Particle Filter Tracker (GPAPF), which is an extension of the annealed particle filter tracker and uses Gaussian Process Dynamical Model (GPDM) in order to reduce the dimensionality of the problem, increase the tracker's stability and learn the motion models. Motion of human body is described by concatenation of low dimensional manifolds which characterize different motion types. The trajectories in the latent space provide low dimensional representations of sequences of body poses performed during motion. Our approach uses these trajectories in order to classify human actions. The approach was checked on HumanEva data set as well as on our own one. The results and the comparison to other methods are presented.