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We present a background
subtraction method that uses multiple cues to robustly detect objects
in adverse conditions. The algorithm consists of three distinct levels
i.e pixel level, region level and frame level. At the pixel level, statistical
models of gradients and color are separately used to classify each pixel
as belonging to background or foreground. In region level, foreground
pixels obtained from the color based subtraction are grouped into regions
and gradient based subtraction is then used to make inferences about the
validity of these regions. Pixel based models are updated based on decisions
made at the region level. Finally frame level analysis is performed to
detect global illumination changes. Our method provides the solution to
some of the common problems that are not addressed by most background
subtraction algorithms such as quick illumination changes, repositioning
of static background objects, and initialization of background model with
moving objects present in the scene. Results
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Related Publications:
Omar Javed , Khurram Shafique and Mubarak Shah, "A
Hierarchical Approach to Robust Background Subtraction using Color and
Gradient Information", IEEE Workshop on Motion and Video Computing,
Orlando, Dec 5-6 2002
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