CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Generative modeling for continuous non-linearly embedded visual inference
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Discriminative Density Propagation for 3D Human Motion Estimation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Monocular Human Motion Capture with a Mixture of Regressors
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Dynamic Appearance Modeling for Human Tracking
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Transformation invariant component analysis for binary images
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Computational studies of human motion: part 1, tracking and motion synthesis
Foundations and Trends® in Computer Graphics and Vision
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
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
A local basis representation for estimating human pose from cluttered images
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Multivariate relevance vector machines for tracking
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Monocular tracking of 3d human motion with a coordinated mixture of factor analyzers
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Learning local models for 2D human motion tracking
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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We consider the problem of monocular 3d body pose tracking from video sequences. This task is inherently ambiguous. We propose to learn a generative model of the relationship of body pose and image appearance using a sparse kernel regressor. Within a particle filtering framework, the potentially multimodal posterior probability distributions can then be inferred. The 2d bounding box location of the person in the image is estimated along with its body pose. Body poses are modelled on a low-dimensional manifold, obtained by LLE dimensionality reduction. In addition to the appearance model, we learn a prior model of likely body poses and a nonlinear dynamical model, making both pose and bounding box estimation more robust. The approach is evaluated on a number of challenging video sequences, showing the ability of the approach to deal with low-resolution images and noise.