View-Invariant Representation and Recognition of Actions
International Journal of Computer Vision
On the Representation and Matching of Qualitative Shape at Multiple Scales
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
The Representation and Recognition of Human Movement Using Temporal Templates
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Recognition of human body motion using phase space constraints
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Visual panel: virtual mouse, keyboard and 3D controller with an ordinary piece of paper
Proceedings of the 2001 workshop on Perceptive user interfaces
Dynamic Human Pose Estimation using Markov Chain Monte Carlo Approach
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
Space-Time Behavior Based Correlation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
3D human pose from silhouettes by relevance vector regression
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Discriminative human action recognition in the learned hierarchical manifold space
Image and Vision Computing
Automatic key pose selection for 3D human action recognition
AMDO'10 Proceedings of the 6th international conference on Articulated motion and deformable objects
A survey of vision-based methods for action representation, segmentation and recognition
Computer Vision and Image Understanding
Real-time classification of dance gestures from skeleton animation
SCA '11 Proceedings of the 2011 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Recognition and segmentation of 3-d human action using HMM and multi-class adaboost
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Model-based recognition of human actions by trajectory matching in phase spaces
Image and Vision Computing
Hi-index | 0.00 |
Our goal is automatic recognition of basic human actions, such as stand, sit and wave hands, to aid in natural communication between a human and a computer. Human actions are inferred from human body joint motions, but such data has high dimensionality and large spatial and temporal variations may occur in executing the same action. We present a learning-based approach for the representation and recognition of 3D human action. Each action is represented by a template consisting of a set of channels with weights. Each channel corresponds to the evolution of one 3D joint coordinate and its weight is learned according to the Neyman-Pearson criterion. We use the learned templates to recognize actions based on χ2 error measurement. Results of recognizing 22 actions on a large set of motion capture sequences as well as several annotated and automatically tracked sequences show the effectiveness of the proposed algorithm.