An efficient probabilistic context-free parsing algorithm that computes prefix probabilities
Computational Linguistics
A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of Visual Activities and Interactions by Stochastic Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Recognition of Group Activities using Dynamic Probabilistic Networks
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
CASEE: a hierarchical event representation for the analysis of videos
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Stochastic grammar has been used in many video analysis and event recognition applications as an efficient model to represent large-scale video activity. However, in previous works, due to the limitation on representing parallel temporal relations, traditional stochastic grammar cannot be used to model complex multi-agent activity including parallel temporal relations between sub-activities (such as “during” relation). In this paper, we extend the traditional grammar by introducing Temporal Relation Events (TRE) to solve the problem. The corresponding grammar parser appending complex temporal inference is also proposed. A system that can recognize two hands’ cooperative action in a “telephone calling” activity is built to demonstrate the effectiveness of our methods. In the experiment, a simple method to model the explicit state duration probability distribution in HMM detector is also proposed for accurate primitive events detection.