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
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
Probabilistic model-based background subtraction
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Human action recognition in table-top scenarios: an HMM-based analysis to optimize the performance
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
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There is biological evidence that human actions are composed out of action primitives, like words and sentences being composed out of phonemes. Similarly to language processing, one possibility to model and recognize complex actions is to use grammars with action primitives as the alphabet. A major challenge here is that the action primitives need to be recovered first from the noisy input signal before further processing with the action grammar can be done. In this paper we combine a Hidden Markov Model-based approach with a simplified version of a condensation algorithm which allows to recover the action primitives in an observed 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 required. We verify our approach on a large database. Recognition rates are slightly lower than the rate when recognizing the singular action primitives.