lecture22 - Change Detection C Stauffer and W.E.L Grimson...

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Change Detection C. Stauffer and W.E.L. Grimson, “Learning patterns of activity using real time tracking,” IEEE Trans. On PAMI, 22(8):747-757, Aug 2000
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Motivation Detection of interesting objects in videos is the first step in the process of automated surveillance and tracking. Focus of attention method greatly reduces the processing-time required for tracking and activity recognition.
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Introduction Objectives: Given a sequence of images from a stationary camera identify pixels comprising ‘moving’ objects. We call the pixels comprising ‘moving’ objects as ‘foreground pixels’ and the rest as ‘background pixels’ General Solution Model properties of the scene (e.g. color, texture e.t.c) at each pixel. Significant change in the properties indicates an interesting change.
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Introduction Problems in Realistic situations: Moving but uninteresting objects e.g. trees, flags or grass. Long term illumination changes e.g. time of day. Quick illumination changes e.g. cloudy weather Shadows Other Physical changes in the background e.g. dropping or picking up of objects Initialization
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Issues Adaptivity Background model must be adaptive to changes in background. Multiple Models Multiple processes generate color at every pixel. The background model should be able to account for these processes. Weighting the observations (models) The system must be able to weight the observation to make decisions. For example, the observations made a long time back should have less weight than the recent observations. Similarly, the frequent observations are more important than the ones with less occurrence.
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Color based Background Modeling Pixel level Color Modeling Multiple Processes are generating color ‘ x’ at each pixel Where x=[R,G,B] T Time =T Pixel(x,y)=blue Time =T+1 pixel(x,y)=green
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