Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Learning to track 3D human motion from silhouettes
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
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
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
Recovering 3D Human Pose from Monocular Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Generative and Discriminative Models in a Framework for Articulated Pose Estimation
International Journal of Computer Vision
Impact of Dynamics on Subspace Embedding and Tracking of Sequences
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Learning Joint Top-Down and Bottom-up Processes for 3D Visual Inference
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Gaussian Process Dynamical Models for Human Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Multivariate relevance vector machines for tracking
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
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A novel framework based on action recognition feedback for pose reconstruction of articulated human body from monocular images is proposed in this paper. The intrinsic ambiguity caused by perspective projection makes it difficult to accurately recover articulated poses from monocular images. To alleviate such ambiguity, we exploit the high-level motion knowledge as action recognition feedback to discard those implausible estimates and generate more accurate pose candidates using large number of motion constraints during natural human movement. The motion knowledge is represented by both local and global motion constraints. The local spatial constraint captures motion correlation between body parts by multiple relevance vector machines while the global temporal constraint preserves temporal coherence between time-ordered poses via a manifold motion template. Experiments on the CMU Mocap database demonstrate that our method performs better on estimation accuracy than other methods without action recognition feedback.