A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Robust and Efficient Foreground Analysis for Real-Time Video Surveillance
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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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Presented framework provides a method for adaptive background change detection in video from monocular static cameras. A background change constitutes of objects left in the scene and objects moved or taken from the scene. This framework may be applied to luggage left behind in public places, to asses the damage and theft of public property, or to detect minute changes in the scene. The key elements of the framework include spatiotemporal motion detection, texture classification of non-moving regions, and spatial clustering of detected background changes. Motion detection based on local variation of spatiotemporal texture separates the foreground and background regions. Local background dissimilarity measurement is based on wavelet decomposition of localized texture maps. Dynamic threshold of the normalized dissimilarity measurement identifies changed local background blocks, and spatial clustering isolates the regions of interest. The results are demonstrated on the PETS 2006 video sequences.