Compilers: principles, techniques, and tools
Compilers: principles, techniques, and tools
A method for the specification and parsing of visual languages
A method for the specification and parsing of visual languages
Object identification: a Bayesian analysis with application to traffic surveillance
Artificial Intelligence - Special issue: artificial intelligence 40 years later
Recognition of Visual Activities and Interactions by Stochastic Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing planned multiperson action
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Image Segmentation by Data-Driven Markov Chain Monte Carlo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual Event Classification via Force Dynamics
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Video Sequence Interpretation for Visual Surveillance
VS '00 Proceedings of the Third IEEE International Workshop on Visual Surveillance (VS'2000)
Recognition of Group Activities using Dynamic Probabilistic Networks
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Robust Visual Tracking by Integrating Multiple Cues Based on Co-Inference Learning
International Journal of Computer Vision - Special Issue on Computer Vision Research at the Beckman Institute of Advanced Science and Technology
Stochastic attribute-value grammars
Computational Linguistics
Hypothesis Selection for Scene Interpretation Using Grammatical Models of Scene Evolution
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Video-based event recognition: activity representation and probabilistic recognition methods
Computer Vision and Image Understanding - Special issue on event detection in video
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
TemporalBoost for Event Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Bottom-up/Top-Down Image Parsing by Attribute Graph Grammar
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Problem-Solving Methods in Artificial Intelligence
Problem-Solving Methods in Artificial Intelligence
Attribute Grammar-Based Event Recognition and Anomaly Detection
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
A stochastic grammar of images
Foundations and Trends® in Computer Graphics and Vision
Semantic event representation and recognition using syntactic attribute graph grammar
Pattern Recognition Letters
Detecting Carried Objects in Short Video Sequences
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Event Modeling and Recognition Using Markov Logic Networks
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
A bibliographical study of grammatical inference
Pattern Recognition
Tracking multiple humans in crowded environment
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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
Mining Layered Grammar Rules for Action Recognition
International Journal of Computer Vision
Robust abandoned object detection integrating wide area visual surveillance and social context
Pattern Recognition Letters
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We propose a method for disambiguating uncertain detections of events by seeking global explanations for activities. Given a noisy visual input, and exploiting our knowledge of the activity and its constraints, one can provide a consistent set of events explaining all the detections. The paper presents a complete framework that starts with a general way to formalise the set of global explanations for a given activity using attribute multiset grammars (AMG). An AMG combines the event hierarchy with the necessary features for recognition and algebraic constraints defining allowable combinations of events and features. Parsing a set of detections by such a grammar finds a consistent set of events that satisfies the activity's constraints. Each parse tree has a posterior probability in a Bayesian sense. To find the best parse tree, the grammar and a finite set of detections are mapped into a Bayesian network. The set of possible labellings of the Bayesian network corresponds to the set of all parse trees for a given set of detections. We compare greedy, multiple-hypotheses trees, reversible jump MCMC, and integer programming for finding the Maximum a Posteriori (MAP) solution over the space of explanations. The framework is tested for two applications; the activity in a bicycle rack and around a building entrance.