ECCV 90 Proceedings of the first european conference on Computer vision
Robust computation of optical flow in a multi-scale differential framework
International Journal of Computer Vision
An Active Testing Model for Tracking Roads in Satellite Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Independent Motion: The Statistics of Temporal Continuity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Salient Motion by Accumulating Directionally-Consistent Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic extraction of roads from aerial images based on scale space and snakes
Machine Vision and Applications
Robot Vision
Optimally Rotation-Equivariant Directional Derivative Kernels
CAIP '97 Proceedings of the 7th International Conference on Computer Analysis of Images and Patterns
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
Analysis of Persistent Motion Patterns Using the 3D Structure Tensor
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
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Video provides strong cues for automatic road extraction that are not available in static aerial images. In video from a static camera, or stabilized (or geo-referenced) aerial video data, motion patterns within a scene enable function attribution of scene regions. A "road", for example, may be defined as a path of consistent motion -- a definition which is valid in a large and diverse set of environments. The spatio-temporal structure tensor field is an ideal representation of the image derivative distribution at each pixel because it can be updated in real time as video is acquired. An eigen-decomposition of the structure tensor encodes both the local scene motion and the variability in the motion. Additionally, the structure tensor field can be factored into motion components, allowing explicit determination of traffic patterns in intersections. Example results of a real time system are shown for an urban scene with both well-traveled and infrequently traveled roads, indicating that both can be discovered simultaneously. The method is ideal in urban traffic scenes, which are the most difficult to analyze using static imagery.