Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
The visual analysis of human movement: a survey
Computer Vision and Image Understanding
Human motion analysis: a review
Computer Vision and Image Understanding
A survey of computer vision-based human motion capture
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Implicit Probabilistic Models of Human Motion for Synthesis and Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
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 3-D Tracking of the Golf Swing
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Recovering 3D Human Body Configurations Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Efficient visual object tracking with online nearest neighbor classifier
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Efficient and robust shape matching for model based human motion capture
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
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We present a method for 3D pose estimation of human motion in generative framework. For the generalization of application scenario, the observation information we utilized comes from monocular silhouettes. We distill prior information of human motion by performing conventional PCA on single motion capture data sequence. In doing so, the aims for both reducing dimensionality and extracting the prior knowledge of human motion are achieved simultaneously. We adopt the shape contexts descriptor to construct the matching function, by which the validity and the robustness of the matching between image features and synthesized model features can be ensured. To explore the solution space efficiently, we design the Annealed Genetic Algorithm (AGA) and Hierarchical Annealed Genetic Algorithm (HAGA) that searches the optimal solutions effectively by utilizing the characteristics of state space. Results of pose estimation on different motion sequences demonstrate that the novel generative method can achieves viewpoint invariant 3D pose estimation.