CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Multiperspective Thermal IR and Video Arrays for 3D Body Tracking and Driver Activity Analysis
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Facial Action Coding Using Multiple Visual Cues and a Hierarchy of Particle Filters
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Determining driver visual attention with one camera
IEEE Transactions on Intelligent Transportation Systems
Real-time system for monitoring driver vigilance
IEEE Transactions on Intelligent Transportation Systems
Looking-In and Looking-Out of a Vehicle: Computer-Vision-Based Enhanced Vehicle Safety
IEEE Transactions on Intelligent Transportation Systems
Lane Change Intent Analysis Using Robust Operators and Sparse Bayesian Learning
IEEE Transactions on Intelligent Transportation Systems
Model-based human-centered task automation: a case study in ACC system design
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A probabilistic framework for modeling and real-time monitoring human fatigue
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Using personas and scenarios as an interface design tool for advanced driver assistance systems
UAHCI'07 Proceedings of the 4th international conference on Universal access in human-computer interaction: ambient interaction
Intelligent vehicle handling: steering and body postures while cornering
ARCS'08 Proceedings of the 21st international conference on Architecture of computing systems
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Driving is a very complex task which, at its core, involves the interaction between the driver and his/her environment. It is therefore extremely important to develop driver assistance systems that are centered around the driver from the ground up. In this paper, we explore one aspect of such a system. Specifically, we focus on monitoring the driver's face and facial regions. We demonstrate a real-world system for tracking face and facial regions and provide insight as to it importance and placement in human-centered driver assistance systems. Result demonstrating its impact on driver assistance systems as well as its performance in real-world driving scenarios are shown.