Coloured Petri nets: basic concepts, analysis methods and practical use, volume 3
Coloured Petri nets: basic concepts, analysis methods and practical use, volume 3
A survey of computer vision-based human motion capture
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Recognizing planned multiperson action
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Action Recognition Using Probabilistic Parsing
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Human Motion: Modeling and Recognition of Actions and Interactions
3DPVT '04 Proceedings of the 3D Data Processing, Visualization, and Transmission, 2nd International Symposium
Representation and Recognition of Events in Surveillance Video Using Petri Nets
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 7 - Volume 07
Automatic Analysis of Multimodal Group Actions in Meetings
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analysis of Player Actions in Selected Hockey Game Situations
CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
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
Learning Temporal Sequence Model from Partially Labeled Data
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Activity Recognition using Dynamic Bayesian Networks with Automatic State Selection
WMVC '07 Proceedings of the IEEE Workshop on Motion and Video Computing
Closed-world tracking of multiple interacting targets for indoor-sports applications
Computer Vision and Image Understanding
A trajectory-based analysis of coordinated team activity in a basketball game
Computer Vision and Image Understanding
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
Stochastic Petri Nets: Modelling, Stability, Simulation
Stochastic Petri Nets: Modelling, Stability, Simulation
Detecting and discriminating behavioural anomalies
Pattern Recognition
Propagating uncertainty in Petri nets for activity recognition
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
Team activity recognition in sports
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
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This paper presents the use of place/transition petri nets (PNs) for the recognition and evaluation of complex multi-agent activities. The PNs were built automatically from the activity templates that are routinely used by experts to encode domain-specific knowledge. The PNs were built in such a way that they encoded the complex temporal relations between the individual activity actions. We extended the original PN formalism to handle the propagation of evidence using net tokens. The evaluation of the spatial and temporal properties of the actions was carried out using trajectory-based action detectors and probabilistic models of the action durations. The presented approach was evaluated using several examples of real basketball activities. The obtained experimental results suggest that this approach can be used to determine the type of activity that a team has performed as well as the stage at which the activity ended.