Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
International Journal of Computer Vision
Optical Flow Estimation and Segmentation of Multiple Moving Dynamic Textures
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Efficient adaptive density estimation per image pixel for the task of background subtraction
Pattern Recognition Letters
Sequential Change Detection on Data Streams
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Modeling, Clustering, and Segmenting Video with Mixtures of Dynamic Textures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scene modeling and change detection in dynamic scenes: A subspace approach
Computer Vision and Image Understanding
Detection of multiple dynamic textures using feature space mapping
IEEE Transactions on Circuits and Systems for Video Technology
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Research on temporal textures has concerned mainly modeling, synthesis and detection, but not finding changes between different temporal textures. Shot change detection, based on appearance, has received much research attention, but detection of changes between temporal textures has not been addressed sufficiently. Successive temporal textures in a video often have a similar appearance but different motion, a change that shot change detection cannot discern. In this paper, changes between temporal textures are captured by deriving a non-parametric statistical model for the motions via a novel approach, based on properties of the Fourier transform. Motion statistics are used in a sequential change detection test to find changes in the motion distributions, and consequently the temporal textures. Experiments use a wide range of videos of temporal textures, groups of people, traffic. The proposed approach leads to correct change detection, at a low computational cost.