Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data preparation for data mining
Data preparation for data mining
The context toolkit: aiding the development of context-enabled applications
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing User Context via Wearable Sensors
ISWC '00 Proceedings of the 4th IEEE International Symposium on Wearable Computers
What Shall We Teach Our Pants?
ISWC '00 Proceedings of the 4th IEEE International Symposium on Wearable Computers
Robust Real-Time Face Detection
International Journal of Computer Vision
A Comprehensive Context Model for Next Generation Ubiquitous Computing Applications
RTCSA '05 Proceedings of the 11th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications
Activity Recognition using Dynamic Bayesian Networks with Automatic State Selection
WMVC '07 Proceedings of the IEEE Workshop on Motion and Video Computing
Hidden Markov Models for Activity Recognition in Ambient Intelligence Environments
ENC '07 Proceedings of the Eighth Mexican International Conference on Current Trends in Computer Science
Accurate activity recognition in a home setting
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
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Smart cars are promising application domain for ubiquitous computing. Context recognition is important support for a smart car to avoid accidents proactively. Despite many techniques have been developed, we find a lack of complex situation recognition in the smart car environment. This paper presents a novel context recognition approach that is composed of two parts: offline statistic-based situation pattern training and online situation recognition. The training phase is done to learn the statistical relationship between simple context atoms and complex context situations and hence generate the pattern of every single situation. The online recognition phase will recognize the current situation according to its pattern in the running time of a smart car. The implementation of the software and prototype is given to provide the running environment for the approach. Performance evaluation shows that our approach is effective and applicable in a smart car.