Linear programming for matching in human body gesture recognition

Authors:
Hao Jiang;Ze-Nian Li;Mark S. Drew
Affiliations:
School of Computing Science, Simon Fraser University, Burnaby, BC, Canada;School of Computing Science, Simon Fraser University, Burnaby, BC, Canada;School of Computing Science, Simon Fraser University, Burnaby, BC, Canada
Venue:
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
Year:
2005

Citing 10
Cited 0

Approximation algorithms for the metric labeling problem via a new linear programming formulation

SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Structural Graph Matching Using the EM Algorithm and Singular Value Decomposition

IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Fast Approximate Energy Minimization via Graph Cuts

IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to Parse Pictures of People

ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Estimating Human Body Configurations Using Shape Context Matching

ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Approximation Algorithms for Classification Problems with Pairwise Relationships: Metric Labeling and Markov Random Fields

FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Real-Time Motion Analysis with Linear-Programming

ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Recovering human body configurations: combining segmentation and recognition

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Shape-from-silhouette of articulated objects and its use for human body kinematics estimation and motion capture

CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a novel human body gesture recognition method using a linear programming based matching scheme. Instead of attempting to segment an object from the background, we develop a novel successive convexification linear programming method to locate the target by searching for the best matching region based on a graph template. The linear programming based matching scheme generates relatively dense matching patterns and thus presents a key feature for robust object matching and human body gesture recognition. By matching distance transformations of edge maps, the proposed scheme is able to match figures with large appearance changes. We further present gesture recognition methods based on the similarity of the exemplar with the matching target. Experiments show promising results for recognizing human body gestures in cluttered environments.