Hierarchical Vibrations: A Structural Decomposition Approach for Image Analysis
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Stable Structural Deformations
ICVS '09 Proceedings of the 7th International Conference on Computer Vision Systems: Computer Vision Systems
Hierarchical vibrations for part-based recognition of complex objects
Pattern Recognition
Robust Pose Recognition of the Obscured Human Body
International Journal of Computer Vision
We are family: joint pose estimation of multiple persons
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
A structural filter approach to human detection
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Efficient inference with multiple heterogeneous part detectors for human pose estimation
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Finding human poses in videos using concurrent matching and segmentation
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Upper Body Detection and Tracking in Extended Signing Sequences
International Journal of Computer Vision
2D Articulated Human Pose Estimation and Retrieval in (Almost) Unconstrained Still Images
International Journal of Computer Vision
Discriminative Appearance Models for Pictorial Structures
International Journal of Computer Vision
Using linking features in learning non-parametric part models
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Discriminative hierarchical part-based models for human parsing and action recognition
The Journal of Machine Learning Research
Hi-index | 0.00 |
Tree-structured models have been widely used for human pose estimation, in either 2D or 3D. While such models allow efficient learning and inference, they fail to capture additional dependencies between body parts, other than kinematic constraints between connected parts. In this paper, we consider the use of multiple tree models, rather than a single tree model for human pose estimation. Our model can alleviate the limitations of a single tree-structured model by combining information provided across different tree models. The parameters of each individual tree model are trained via standard learning algorithms in a single tree-structured model. Different tree models can be combined in a discriminative fashion by a boosting procedure. We present experimental results showing the improvement of our approaches on two different datasets. On the first dataset, we use our multiple tree framework for occlusion reasoning. On the second dataset, we combine multiple deformable trees for capturing spatial constraints between non-connected body parts.