Vision and Navigation: The Carnegie Mellon Navlab
Vision and Navigation: The Carnegie Mellon Navlab
An Adaptive Fusion Architecture for Target Tracking
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
An eye localization, tracking and blink pattern recognition system: Algorithm and evaluation
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Multimodal advanced driver assistance systems: an overview
Proceedings of the 2nd international workshop on Multimodal interfaces for automotive applications
Online vigilance analysis combining video and electrooculography features
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
Exploiting temporal and spatial constraints in traffic sign detection from a moving vehicle
Machine Vision and Applications
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At the Australian National University's Intelligent Vehicle Project, we are developing subsystems for: driver fatigue or inattention detection; pedestrian spotting; blind-spot checking and merging assistance to validate whether sufficient clearance exists between cars; driver feedback for lane keeping; computer-augmented vision (that is, lane boundary or vehicle highlighting on a head-up display); traffic sign detection and recognition; and human factors research aids Systems that perform such supporting tasks are generally called driver assistance systems (DAS). We believe that implementing DAS could prevent similar accidents or at least reduce their severity.