The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer vision for computer games
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Real-time Human Motion Analysis by Image Skeletonization
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Visual panel: virtual mouse, keyboard and 3D controller with an ordinary piece of paper
Proceedings of the 2001 workshop on Perceptive user interfaces
Segmenting motion capture data into distinct behaviors
GI '04 Proceedings of the 2004 Graphics Interface Conference
3D Shape Context Based Gesture Analysis Integrated with Tracking using Omni Video Array
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Proceedings of the 2006 ACM SIGGRAPH/Eurographics symposium on Computer animation
A differential geometric approach to representing the human actions
Computer Vision and Image Understanding
View Invariant Human Action Recognition Based on Factorization and HMMs
IEICE - Transactions on Information and Systems
A Single Camera Motion Capture System for Human-Computer Interaction
IEICE - Transactions on Information and Systems
Efficient and robust annotation of motion capture data
Proceedings of the 2009 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Proceedings of the 2008 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Variable silhouette energy image representations for recognizing human actions
Image and Vision Computing
Discriminative human action recognition in the learned hierarchical manifold space
Image and Vision Computing
EyeScreen: a gesture interface for manipulating on-screen objects
HCI'07 Proceedings of the 12th international conference on Human-computer interaction: intelligent multimodal interaction environments
Human motion recognition based on hidden Markov models
ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
Action Recognition in Videos Using Nonnegative Tensor Factorization
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Learning dynamics for exemplar-based gesture recognition
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Real-time classification of dance gestures from skeleton animation
SCA '11 Proceedings of the 2011 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Perceptive user interface, a generic approach
ICCV'05 Proceedings of the 2005 international conference on Computer Vision in Human-Computer Interaction
Vision-Based interpretation of hand gestures for remote control of a computer mouse
ECCV'06 Proceedings of the 2006 international conference on Computer Vision in Human-Computer Interaction
FaceMouse: a human-computer interface for tetraplegic people
ECCV'06 Proceedings of the 2006 international conference on Computer Vision in Human-Computer Interaction
Real-time human pose recognition in parts from single depth images
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
MIMiC: Multimodal Interactive Motion Controller
IEEE Transactions on Multimedia
Activity Modeling Using Event Probability Sequences
IEEE Transactions on Image Processing
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This paper proposes a new examplar-based method for real-time human motion recognition using Motion Capture (MoCap) data. We have formalized streamed recognizable actions, coming from an online MoCap engine, into a motion graph that is similar to an animation motion graph. This graph is used as an automaton to recognize known actions as well as to add new ones. We have defined and used a spatio-temporal metric for similarity measurements to achieve more accurate feedbacks on classification. The proposed method has the advantage of being linear and incremental, making the recognition process very fast and the addition of a new action straightforward. Furthermore, actions can be recognized with a score even before they are fully completed. Thanks to the use of a skeleton-centric coordinate system, our recognition method has become view-invariant. We have successfully tested our action recognition method on both synthetic and real data. We have also compared our results with four state-of-the-art methods using three well known datasets for human action recognition. In particular, the comparisons have clearly shown the advantage of our method through better recognition rates.