Multidimensional Orientation Estimation with Applications to Texture Analysis and Optical Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reconstruction of Image Structure in Presence of Specular Reflections
Proceedings of the 23rd DAGM-Symposium on Pattern Recognition
W4: A Real Time System for Detecting and Tracking People
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Segmenting Foreground Objects from a Dynamic Textured Background via a Robust Kalman Filter
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Background Modeling and Subtraction of Dynamic Scenes
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Road extraction from motion cues in aerial video
Proceedings of the 12th annual ACM international workshop on Geographic information systems
Motion-based background subtraction using adaptive kernel density estimation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Euclidean path modeling for video surveillance
Image and Vision Computing
Scene modeling and change detection in dynamic scenes: A subspace approach
Computer Vision and Image Understanding
Detecting motion patterns via direction maps with application to surveillance
Computer Vision and Image Understanding
Unsupervised view and rate invariant clustering of video sequences
Computer Vision and Image Understanding
Super-resolution without explicit subpixel motion estimation
IEEE Transactions on Image Processing
Motion pattern extraction and event detection for automatic visual surveillance
Journal on Image and Video Processing - Special issue on advanced video-based surveillance
A multi-resolution approach for massively-parallel hardware-friendly optical flow estimation
Journal of Visual Communication and Image Representation
Going with the flow: pedestrian efficiency in crowded scenes
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
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Surveillance applications often capture video over long time periods; interpretation of this data is facilitated by background models that effectively represent the typical behavior in the scene. Capturing statistics of the spatio-temporal derivatives at each pixel can efficiently model surprisingly complicated motion patterns. Considering the video as a function of space and time, the mean 3D structure tensor at each pixel characterizes local image variation, the most common local motion, and whether that motion is consistent or ambiguous. Furthermore, this structure tensor field - the structure tensor at each pixel - is interpretable as a constrained Gaussian probability density function over the derivatives measured across the entire image. In scenes with multiple global motion patterns, a mixture model (of these global distributions) automatically factors background motion into a set of flow fields corresponding to the different motions. The models are developed online in real time and can adapt to changes in background motion. We demonstrate the ability to automatically discover the different motion patterns in an intersection.