Background Subtraction Using Ensembles of Classifiers with an ...

CHAPTER 3FEATURESStandard bitmaps store a three dimensional vector of features per pixel: red, green, and blueintensity values. In this work the number of features per pixels will be increased through nonlineartransformations in order to provide separate feature biases of the scenes being observed.A summary of the image features that will be used in this work can be found in Table 3.1.Using these features gives a total of 13 features per pixel. Each of these features will be used bya separate classifier that only has knowledge of its respective feature.3.1 Gradient FeaturesGradient features are used to detect edges and peaks over intensity changes in images. For thebackground classifiers, each pixel in the image frame will be characterized by the non-thresholdedvalues from the magnitude and orientation values of the Canny edge detector [44]. Using thesetwo features will offer advantages and disadvantages not found using only RGB features. A majoradvantage is found under varying illumination. In Figures 3.3(g) and 3.3(h), a comparison of thegradient magnitude of the same scene with different illumination conditions shows that gradientmagnitude remains largely invariant to the illumination change. This property will allow ourclassifier to be more robust to varying illumination conditions.One disadvantage of using the gradient magnitude is that foreground objects with homogeneousintensities will not appear to change for the classifier in the inner areas of the object. Thisfact leads to a important point about the features being used. Individually, these features do notoffer a significant enough representation of the scene for a classifier to make accurate predictions.Instead, the combination each of these lesser tracking features are assumed to provide a higherrepresentation of the image space when used in an ensemble.20