Recognition of Visual Activities and Interactions by Stochastic Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Maintaining knowledge about temporal intervals
Communications of the ACM
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ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Video-based event recognition: activity representation and probabilistic recognition methods
Computer Vision and Image Understanding - Special issue on event detection in video
Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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EURASIP Journal on Applied Signal Processing
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Semantic Representation and Recognition of Continued and Recursive Human Activities
International Journal of Computer Vision
Sensor-based understanding of daily life via large-scale use of common sense
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Recognition of Users' Activities Using Constraint Satisfaction
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Automatic video interpretation: a novel algorithm for temporal scenario recognition
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Location-based activity recognition using relational Markov networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
A comparison of HMMs and dynamic bayesian networks for recognizing office activities
UM'05 Proceedings of the 10th international conference on User Modeling
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This paper describes a complex event recognition approach with probabilistic reasoning for handling uncertainty. The first advantage of the proposed approach is the flexibility of the modeling of composite events with complex temporal constraints. The second advantage is the use of probability theory providing a consistent framework for dealing with uncertain knowledge for the recognition of complex events. The experimental results show that our system can successfully improve the event recognition rate. We conclude by comparing our algorithm with the state of the art and showing how the definition of event models and the probabilistic reasoning can influence the results of the real-time event recognition.