Estimating Human Body Configurations Using Shape Context Matching
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Fast Pose Estimation with Parameter-Sensitive Hashing
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Pictorial Structures for Object Recognition
International Journal of Computer Vision
Recovering Human Body Configurations Using Pairwise Constraints between Parts
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Guiding Model Search Using Segmentation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
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
Shape Classification Using the Inner-Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian, Exemplar-Based Approach to Hierarchical Shape Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simultaneous Segmentation and Pose Estimation of Humans Using Dynamic Graph Cuts
International Journal of Computer Vision
Category independent object proposals
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Detecting people using mutually consistent poselet activations
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Latent structured models for human pose estimation
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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A scale, rotation and articulation invariant method is proposed to match human subjects in images. Different from the widely used pictorial structure scheme, the proposed method directly matches body parts to image regions which are obtained from object independent proposals and successively merged superpixels. Body part region matching is formulated as a graph matching problem. We globally assign a body part candidate to each node on the model graph so that the overall configuration satisfies the spatial layout of a human body plan, part regions have small overlap, and the part coverage follows proper area ratios. The proposed graph model is non-tree and contains high order hyper-edges. We propose an efficient method that finds global optimal solution to the matching problem with a sequence of branch and bound procedures. The experiments show that the proposed method is able to handle arbitrary scale, rotation, articulation and match human subjects in cluttered images.