The visual analysis of human movement: a survey
Computer Vision and Image Understanding
Inferring 3D Structure with a Statistical Image-Based Shape Model
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Filtering Using a Tree-Based Estimator
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Fast Pose Estimation with Parameter-Sensitive Hashing
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Style-based inverse kinematics
ACM SIGGRAPH 2004 Papers
Learning Appearance Manifolds from Video
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Beyond Trees: Common-Factor Models for 2D Human Pose Recovery
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
A Model-Based Approach for Estimating Human 3D Poses in Static Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Cyclic articulated human motion tracking by sequential ancestral simulation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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
3D human pose from silhouettes by relevance vector regression
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
Human pose estimation from polluted silhouettes using sub-manifold voting strategy
IWICPAS'06 Proceedings of the 2006 Advances in Machine Vision, Image Processing, and Pattern Analysis international conference on Intelligent Computing in Pattern Analysis/Synthesis
Bio-inspired Connectionist Architecture for Visual Detection and Refinement of Shapes
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Shape classification by manifold learning in multiple observation spaces
Information Sciences: an International Journal
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In this paper, a learning-based framework is proposed for human pose estimation in complicated environments. Human silhouettes extracted from input images are always incomplete and corrupted due to shadows, occlusions, motion blur, or foreground/background color similarity. Given a corrupted body silhouette, our goal is to infer the corresponding pose structure robustly, and to reconstruct the input silhouette as well. The basic assumption of our method is that the body pose (and configuration) can be indicated by some parts (components) of the silhouette given a training data set. Based on this assumption, a robust statistical method is applied to gather the information from uncorrupted components, and to ignore the effects from the outliers. In this method, Gaussian Process is used to learn the low-dimensional manifold of visual input data, and to create the sub-manifold corresponding to each component of the silhouette. Different from traditional methods, the likelihood probability is computed by means of a sub-manifold voting strategy based on the learned sub-manifolds. By fusing the likelihood and the prior of human poses, the proposed learning-based framework can specify the location of the input human pose in the latent space. The intrinsic pose and configuration can then be deduced from this location, or be refined after outlier rejection. Experiments show that our approach has a great ability to estimate human poses from corrupted silhouettes with small computational burden. Therefore, it can be applied for tracking initialization, 3D pose estimation, 2D configuration reconstruction in occluded, shadowed and noisy environments.