From image sequences towards conceptual descriptions
Image and Vision Computing
An efficient probabilistic context-free parsing algorithm that computes prefix probabilities
Computational Linguistics
Recognition of Visual Activities and Interactions by Stochastic Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Connectionist Speech Recognition: A Hybrid Approach
Connectionist Speech Recognition: A Hybrid Approach
Hidden Markov Models for Speech Recognition
Hidden Markov Models for Speech Recognition
View-Invariant Representation and Recognition of Actions
International Journal of Computer Vision
Human motion analysis for biomechanics and biomedicine
Machine Vision and Applications - Special issue: Human modeling, analysis, and synthesis
Repetitive Motion Analysis: Segmentation and Event Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding motion primitives in human body gestures
GW'05 Proceedings of the 6th international conference on Gesture in Human-Computer Interaction and Simulation
Key frame-based activity representation using antieigenvalues
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Decomposition of human motion into dynamics-based primitives with application to drawing tasks
Automatica (Journal of IFAC)
| Hi-index | 0.00 |
There is biological evidence that human actions are composed out of action primitives, similarly to words and sentences being composed out of phonemes. Given a set of action primitives and an action composed out of these primitives we present a Hidden Markov Model-based approach that allows to recover the action primitives in that action. In our approach, the primitives may have different lengths, no clear “divider” between the primitives is necessary. The primitive detection is done online, no storing of past data is necessary. We verify our approach on a large database. Recognition rates are slightly smaller than the rate when recognizing the singular action primitives.