On the use of motion concepts for top-down control in traffic scenes
ECCV 90 Proceedings of the first european conference on Computer vision
Visual surveillance in a dynamic and uncertain world
Artificial Intelligence - Special volume on computer vision
Learning the distribution of object trajectories for event recognition
BMVC '95 Proceedings of the 6th British conference on Machine vision (Vol. 2)
The Active Recovery of 3D Motion Trajectories and Their Use in Prediction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Interpreting a dynamic and uncertain world: task-based control
Artificial Intelligence
Computer Vision and Image Understanding
Spatiotemporally Adaptive Estimation and Segmenation of OF-Fields
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
(Mis?-) Using DRT for Generation of Natural Language Text from Image Sequences
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Generation of Semantic Regions from Image Sequences
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Using Adaptive Tracking to Classify and Monitor Activities in a Site
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Curve Finder Combining Perceptual Grouping and a Kalman Like Fitting
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Building Qualitative Event Models Automatically from Visual Input
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Model-based tracking of complex innercity road intersections
Mathematical and Computer Modelling: An International Journal
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Segmentation of optical flow fields, estimated by spatiotemporally adaptive methods, is - under favourable conditions - reliable enough to track moving vehicles at intersections without using vehicle or road models. Already a single image plane trajectory per lane obtained in this manner offers valuable information about where lane markers should be searched for. Fitting a hyperbola to an image plane trajectory of a vehicle which crosses an intersection thus provides concise geometric hints. These allow to separate images of direction indicators and of stop marks painted onto the road surface from side marks delimiting a lane. Such a 'lane spine hyperbola', moreover, facilitates to link side marks even across significant gaps in cluttered areas of a complex intersection. Data-driven extraction of trajectory information thus facilitates to link local spatial descriptions practically across the entire field of view in order to create global spatial descriptions. These results are important since they allow to extract required information from image sequences of traffic scenes without the necessity to obtain a map of the road structure and to make this information (interactively) available to a machine-vision-based traffic surveillance system. The approach is illustrated for different lanes with markings which are only a few pixels wide and thus diffcult to detect reliably without the search area restriction provided by a lane spine hyperbola. So far, the authors did not find comparable results in the literature.