Active shape models and the shape approximation problem
BMVC '95 Proceedings of the 1995 British conference on Machine vision (Vol. 1)
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Fast Pose Estimation with Parameter-Sensitive Hashing
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Face Recognition with MRC-Boosting
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Recovering 3D Human Pose from Monocular Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparison of Silhouette Shape Descriptors for Example-based Human Pose Recovery
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Recovering 3D Human Body Configurations Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
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In this paper, we proposed a fast and accurate human pose estimation framework that combines top-down and bottom-up methods. The framework consists of an initialization stage and an iterative searching stage. In the initialization stage, example based method is used to find several initial poses which are used as searching seeds of the next stage. In the iterative searching stage, a larger number of body parts candidates are generated by adding random disturbance to searching seeds. Belief Propagation (BP) algorithm is applied to these candidates to find the best n poses using the information of global graph model and part image likelihood. Then these poses are further used as searching seeds for the next iteration. To model image likelihoods of parts we designed rotation invariant EdgeField features based on which we learnt boosted classifiers to calculate the image likelihoods. Experiment result shows that our framework is both fast and accurate.