Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Learning variable-length Markov models of behavior
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Understanding Motion Capture for Computer Animation and Video Games
Understanding Motion Capture for Computer Animation and Video Games
Computer Vision for Interactive Computer Graphics
IEEE Computer Graphics and Applications
Coupled hidden Markov models for complex action recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Invariant features for 3-D gesture recognition
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Real-time recognition of activity using temporal templates
WACV '96 Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision (WACV '96)
Recognition of Human Periodic Motion " A Frequency Domain Approach
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Learning visual behavior for gesture analysis
ISCV '95 Proceedings of the International Symposium on Computer Vision
Video-based character animation
Proceedings of the 2005 ACM SIGGRAPH/Eurographics symposium on Computer animation
A Quantitative Evaluation of Video-based 3D Person Tracking
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Kinematic jump processes for monocular 3D human tracking
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication
Hand posture recognition using real-time artificial evolution
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
International Journal of Web-Based Learning and Teaching Technologies
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We present a technique for modeling and recognising human activity from moving light displays using hidden Markov models. We extract a small number of joint angles at each frame to form a feature vector. Continuous hidden Markov models are then trained with the resulting time series, one for each of a variety of human activity, using the Baum-Welch algorithm. Motion classification is then attempted by evaluation of the forward variable for each model using previously unseen test data. Experimental results based on real-world human motion capture data demonstrate the performance of the algorithm and some degree of robustness to data noise and human motion irregularity. This technique has potential applications in activity classification for gesture-based game interfaces and character animation.