A State-Based Approach to the Representation and Recognition of Gesture
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
Tools and Techniques for Video Performance Evaluation
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
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
Video-based event recognition: activity representation and probabilistic recognition methods
Computer Vision and Image Understanding - Special issue on event detection in video
An appearance-based approach for consistent labeling of humans and objects in video
Pattern Analysis & Applications
Ontological inference for image and video analysis
Machine Vision and Applications
Video understanding for complex activity recognition
Machine Vision and Applications
A novel approach for recognition of human actions with semi-global features
Machine Vision and Applications
Pattern Recognition Letters
An ontology based approach for activity recognition from video
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Robust Unattended and Stolen Object Detection by Fusing Simple Algorithms
AVSS '08 Proceedings of the 2008 IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance
Annotation Collection and Online Performance Evaluation for Video Surveillance: The ViSOR Project
AVSS '08 Proceedings of the 2008 IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance
Semantic Representation and Recognition of Continued and Recursive Human Activities
International Journal of Computer Vision
An Ontology for Event Detection and its Application in Surveillance Video
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Human action detection via boosted local motion histograms
Machine Vision and Applications
Building Petri nets from video event ontologies
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
A Framework Dealing with Uncertainty for Complex Event Recognition
AVSS '10 Proceedings of the 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance
Human activity analysis: A review
ACM Computing Surveys (CSUR)
A survey of vision-based methods for action representation, segmentation and recognition
Computer Vision and Image Understanding
Propagating uncertainty in Petri nets for activity recognition
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
A Constrained Probabilistic Petri Net Framework for Human Activity Detection in Video*
IEEE Transactions on Multimedia
Machine Recognition of Human Activities: A Survey
IEEE Transactions on Circuits and Systems for Video Technology
Video Event Modeling and Recognition in Generalized Stochastic Petri Nets
IEEE Transactions on Circuits and Systems for Video Technology
Robust Detection of Abandoned and Removed Objects in Complex Surveillance Videos
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Skin detection by dual maximization of detectors agreement for video monitoring
Pattern Recognition Letters
| Hi-index | 0.00 |
This paper presents an approach for real-time video event recognition that combines the accuracy and descriptive capabilities of, respectively, probabilistic and semantic approaches. Based on a state-of-art knowledge representation, we define a methodology for building recognition strategies from event descriptions that consider the uncertainty of the low-level analysis. Then, we efficiently organize such strategies for performing the recognition according to the temporal characteristics of events. In particular, we use Bayesian Networks and probabilistically-extended Petri Nets for recognizing, respectively, simple and complex events. For demonstrating the proposed approach, a framework has been implemented for recognizing human-object interactions in the video monitoring domain. The experimental results show that our approach improves the event recognition performance as compared to the widely used deterministic approach.