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A Hierarchical Approach to Robust Background Subtraction using Color and Gradient Information

 


Omar Javed, Khurram Shafique and Mubarak Shah

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
Quick Illumination Change & Initialization with moving objects Sequence

Video
Mixture of Gaussian Method
Hierarchical Subtraction


Local Illumination Change Sequence


Video
Mixture of Gaussian Method
Hierarchical Subtraction

 

 

Relocation of background Object

Video
Mixture of Gaussian Method
Hierarchical Subtraction


Quick Illumination Change Sequence

Video
Mixture of Gaussian Method
Hierarchical Subtraction

 

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