Motion Detection Based on Local Variation of Spatiotemporal Texture
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 8 - Volume 08
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Comparison of target detection algorithms using adaptive background models
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Robust background subtraction with foreground validation for urban traffic video
EURASIP Journal on Applied Signal Processing
Activity and motion detection based on measuring texture change
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
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In this paper we explore a multi-layer background change detection method based on projections of spatiotemporal 3D texture maps. The aim of this method is to provide a background change detection of a region viewed by multiple cameras. Camera views are projected onto a common ground plane, thus creating a spatially aligned multi-layer background. The aligned multi-layer background is subdivided into non-overlapping texture blocks, and block data is dimensionally reduced by principal component analysis. Motion detection is performed on each block, and non-moving sections of the block are clustered into multiple hyperspheres. An analysis of the clusters from spatially aligned multi-layer blocks reveal regions of changed background. This method is evaluated on surveillance videos available from PETS2006 and PETS2007 datasets.