CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Recognition of Visual Activities and Interactions by Stochastic Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modelling with Generalized Stochastic Petri Nets
Modelling with Generalized Stochastic Petri Nets
Representation of occurrences for road vehicle traffic
Artificial Intelligence
Interpretation of complex situations in a semantic-based surveillance framework
Image Communication
ETISEO, performance evaluation for video surveillance systems
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
Automatic video interpretation: a novel algorithm for temporal scenario recognition
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Analysis of multi-agent activity using petri nets
Pattern Recognition
Building Petri nets from video event ontologies
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
Propagation networks for recognition of partially ordered sequential action
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Particle petri nets for aircraft procedure monitoring under uncertainty
ICATPN'05 Proceedings of the 26th international conference on Applications and Theory of Petri Nets
A Constrained Probabilistic Petri Net Framework for Human Activity Detection in Video
IEEE Transactions on Multimedia
Video Event Modeling and Recognition in Generalized Stochastic Petri Nets
IEEE Transactions on Circuits and Systems for Video Technology
A semantic-based probabilistic approach for real-time video event recognition
Computer Vision and Image Understanding
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Petri Nets is a formalism that has recently been proposed for the specification of models for use in activity recognition . This formalism is attractive because of its inherent ability to model partial ordering, concurrency, logical and temporal relations between the events that compose activities. The main novelty of this work is a probabilistic mechanism (based on the particle filter) for recognizing activities modeled as Petri Nets in video. This mechanism takes into account the observation and semantic uncertainty inherent in low-level events and propagates it into a probabilistic activity recognition.