Continuous Hand Gesture Recognition in the Learned Hierarchical Latent Variable Space

Authors:
Lei Han;Wei Liang
Affiliations:
Department of Computer Science, Beijing Institute of Technology, Beijing, China;Department of Computer Science, Beijing Institute of Technology, Beijing, China
Venue:
AMDO '08 Proceedings of the 5th international conference on Articulated Motion and Deformable Objects
Year:
2008

Citing 9
Cited 0

The Recognition of Human Movement Using Temporal Templates

IEEE Transactions on Pattern Analysis and Machine Intelligence
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data

ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Vision-Based Gesture Recognition: A Review

GW '99 Proceedings of the International Gesture Workshop on Gesture-Based Communication in Human-Computer Interaction
Recognizing Action at a Distance

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data

ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning and Detecting Activities from Movement Trajectories Using the Hierarchical Hidden Markov Models

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Conditional Random Fields for Contextual Human Motion Recognition

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Hidden Conditional Random Fields for Gesture Recognition

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Hierarchical Gaussian process latent variable models

Proceedings of the 24th international conference on Machine learning

Quantified Score

Hi-index	0.00

Visualization

Abstract

We describe a hierarchical approach for recognizing continuous hand gestures. It consists of hierarchical nonlinear dimensionality reduction based feature extraction and Hierarchical Conditional Random Field (Hierarchical CRF) based motion modeling. Articulated hands can be decomposed into several hand parts and we explore the underlying structures of articulated action spaces for both the hand and hand parts using Hierarchical Gaussian Process Latent Variable Model (HGPLVM). In this hierarchical latent variable space, we propose a Hierarchical CRF, which can simultaneously capture the extrinsic class dynamics and learn the relationship between motions of hand parts and class labels, to model the hand motions. Approving recognition performance is obtained on our user-defined hand gesture dataset.