Stereovision-based object segmentation for automotive applications
EURASIP Journal on Applied Signal Processing
Driver Inattention Detection based on Eye Gaze-Road Event Correlation
International Journal of Robotics Research
Using Context to Identify Difficult Driving Situations in Unstructured Environments
FAC '09 Proceedings of the 5th International Conference on Foundations of Augmented Cognition. Neuroergonomics and Operational Neuroscience: Held as Part of HCI International 2009
Estimating the driving state of oncoming vehicles from a moving platform using stereo vision
IEEE Transactions on Intelligent Transportation Systems
A new approach to urban pedestrian detection for automatic braking
IEEE Transactions on Intelligent Transportation Systems
Novel maximum-margin training algorithms for supervised neural networks
IEEE Transactions on Neural Networks
Simultaneous localization and object detection using an a-contrario approach
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Obstacle-Free Pathway Detection by Means of Depth Maps
Journal of Intelligent and Robotic Systems
SVM based MLP neural network algorithm and application in intrusion detection
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
An a-contrario approach for obstacle detection in assistance driving systems
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I
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The early recognition of potentially harmful traffic situations is an important goal of vision-based driver assistance systems. Pedestrians, in particular children, are highly endangered in inner city traffic. Within the DaimlerChrysler urban traffic assistance (UTA) project, we are using stereo vision and motion analysis in order to manage those situations. The flow/depth constraint combines both methods in an elegant way and leads to a robust and powerful detection scheme. A ball bouncing on the road often implies a child crossing the street. Since balls appear very small in the images of our cameras and can move considerably fast, a special algorithm has been developed to achieve maximum recognition reliability.