ECCV 90 Proceedings of the first european conference on 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
Intuitive Visualization and Querying of Cell Motion
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
International Journal of Business Intelligence and Data Mining
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Aerial video provides strong cues for automatic road extraction that are not available in static aerial images. Using stabilized (or geo-referenced) video data, capturing the distribution of spatio-temporal image derivatives gives a powerful, local representation of the scene variation and motion typical at each pixel. This allows a functional attribution of the scene; a "road" is defined as paths of consistent motion --- a definition which is valid in a large and diverse set of environments. Using a classical relationship between image motion and spatio-temporal image derivatives, road features can be extracted as image regions that have significant image variation and a motion consistent with its neighbors. The video pre-processing to generate image derivative distributions over arbitrarily long sequences is implemented in real time on standard laptops, and the flow field computation and interpretation involves a small number of 3 by 3 matrix operations at each pixel location. Example results are shown for an urban scene with both well-traveled and infrequently traveled roads, indicating that both can be discovered simultaneously. This method works robustly in scenes with significant traffic motion and is thus ideal for urban traffic scenes, which often are difficult to analyze using static imagery.