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.
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