Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Artificial Intelligence
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
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ECSQARU '95 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
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CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Real-time American Sign Language recognition from video using hidden Markov models
ISCV '95 Proceedings of the International Symposium on Computer Vision
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Semi-Supervised Adapted HMMs for Unusual Event Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Probabilistic Model for Information and Sensor Validation
The Computer Journal
Detection of defective sources in the setting of possibility theory
Fuzzy Sets and Systems
Context Inferring in the Smart Home: An SWRL Approach
AINAW '07 Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops - Volume 02
Analyzing the combination of conflicting belief functions
Information Fusion
Complexity results and approximation strategies for MAP explanations
Journal of Artificial Intelligence Research
A probabilistic model for sensor validation
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Partially supervised learning by a credal EM approach
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
IEEE Transactions on Signal Processing
Target identification based on the transferable belief model interpretation of dempster-shafer model
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Circuits and Systems for Video Technology
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We address the problem of abnormal behaviour recognition of the inhabitant of a smart home in the presence of unreliable sensors. The corner stone of this work is a two-level architecture sensor fusion based on the Transferable Belief Model (TBM). The novelty of our work lies in the way we detect both unreliable sensors and abnormal behaviour within our architecture by using a temporal analysis of conflict resulting from the fusion of sensors. Detection of abnormal behaviour is based on a prediction/observation process and the influence of the faulty sources is discarded by discounting coefficients. Our architecture is tested in a real-life setting using three heterogeneous sensors enabling the detection of impossible transitions between three possible postures: Sitting, Standing and Lying. The impact of having a faulty sensor management is also tested in the real-life experiment for posture detection.