CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Stochastic Tracking of 3D Human Figures Using 2D Image Motion
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Articulated Body Motion Capture by Stochastic Search
International Journal of Computer Vision
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
Measure Locally, Reason Globally: Occlusion-sensitive Articulated Pose Estimation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A Quantitative Evaluation of Video-based 3D Person Tracking
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Hierarchical Gaussian process latent variable models
Proceedings of the 24th international conference on Machine learning
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
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Human body pose estimation and tracking is a challenging task mainly because of the high dimensionality of the human body model. In this paper we introduce a Hierarchical Annealing Particle Filter (H-APF) algorithm for 3D articulated human body-part tracking. The method exploits Hierarchical Human Body Model (HHBM) in order to perform accurate body pose estimation. The method applies nonlinear dimensionality reduction combined with the dynamic motion model and the hierarchical body model. The dynamic motion model allows to make a better pose prediction, while the hierarchical model of the human body expresses conditional dependencies between the body parts and also allows us to capture properties of separate parts. The improved annealing approach is used for the propagation between different body models and sequential frames. The algorithm was checked on HumanEvaI and HumanEvaII datasets, as well as on other videos and proved to be effective and robust and was shown to be capable of performing an accurate and robust tracking. The comparison to other methods and the error calculations are provided.