Robust recognition and segmentation of human actions using HMMs with missing observations
EURASIP Journal on Applied Signal Processing
Virtual training via vibrotactile arrays
Presence: Teleoperators and Virtual Environments
International Journal of Wireless and Mobile Computing
Shape-Based Human Activity Recognition Using Independent Component Analysis and Hidden Markov Model
IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
Office activity recognition using hand posture cues
BCS-HCI '08 Proceedings of the 22nd British HCI Group Annual Conference on People and Computers: Culture, Creativity, Interaction - Volume 2
View-invariant human activity recognition based on shape and motion features
International Journal of Robotics and Automation
Human motion analysis via statistical motion processing and sequential change detection
Journal on Image and Video Processing - Special issue on video-based modeling, analysis, and recognition of human motion
An unsupervised approach to activity recognition and segmentation based on object-use fingerprints
Data & Knowledge Engineering
Comparison of human and machine recognition of everyday human actions
ICDHM'07 Proceedings of the 1st international conference on Digital human modeling
Independent shape component-based human activity recognition via Hidden Markov Model
Applied Intelligence
Object interaction detection using hand posture cues in an office setting
International Journal of Human-Computer Studies
Action recognition with global features
ICCV'05 Proceedings of the 2005 international conference on Computer Vision in Human-Computer Interaction
Combined classifiers for action recognition
IWICPAS'06 Proceedings of the 2006 Advances in Machine Vision, Image Processing, and Pattern Analysis international conference on Intelligent Computing in Pattern Analysis/Synthesis
Dynamic events as mixtures of spatial and temporal features
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
State of the Art Report on Video-Based Graphics and Video Visualization
Computer Graphics Forum
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A novel method for human activity recognition is presented. Given a video sequence containing human activity, the motion parameters of each frame are first computed using different motion parameter models. The likelihood of these observed motion parameters is optimally approximated, based directly on a multivariate Gaussian probabilistic model. The dynamic change of motion parameter likelihood in a video sequence is characterized using a continuous density hidden Markov model. Activity recognition is then posed as a motion parameter maximum likelihood estimation problem. Experimental results show that the method proposed here works well in recognizing such complex human activities as sitting, getting up from a chair, and some martial art actions.