Background Subtraction Using Ensembles of Classifiers with an ...

epresentations, and various color spaces were explored in order to understand the effects ofaltering the parameters.In [7], Gordon et al. used a Mixture of Gaussians along with depth information from a stereovision system. Adding the depth component demonstrated more effective results in regions withmany foreground objects, but the use of this algorithm is contingent on the implementation ofa stereo vision surveillance system. Similarly, in [8], depth information generated from a stereovision system is used with a Mixture of Gaussians model by Harville. In this method Harville etal. use YUV color space in order to make the algorithm more illumination invariant. Also thelearning rates for each pixel are dynamic in order to allow pixels to adapt at different rates basedon the unique characteristics that the pixel observes.First proposed by Karmann et al. in [9] and later by Ridder et al. in [10], using a Kalman filter[11] to model the background is another popular method of performing background classification.A Kalman filter is a recursive estimator that makes a predication on a future state of a variablebased on previous state information and noise estimation. When used in background estimationeach image pixel is modeled with a Kalman filter. A key advatage of a Kalman filter is its abilityto handle slow illumination changes. Because it recursively updates itself and accounts for noise inthe estimations, slow illumination changes are seamlessly incorporated into the filter. The failingsin a Kalman filter approach to FG/BG segmentation is its ability to handle sharp illuminationchanges. The problem is that in order to handle sharp illumination changes one must increasethe gain on the filter, because increasing the gain allows for a more rapid update. According to[10], when the gain is increased it is not possible to stop foreground objects from being rapidlyadapted to by the filter and becoming modeled as background. So the Kalman filter alone hasno ability to distinguish between sharp background changes and foreground objects.Based on the equations used in [9] and [10], the procedure for using a Kalman filter inbackground modeling is as follows. A pixel p is classified as foreground if |I(p)−ŝ t (p)| > τ, whereτ is the threshold and ŝ t is the Kalman filter prediction at time t. ŝ t is generated using Equations2.11, 2.12, and 2.13. Both α and β are learning rates, where α < β in order to update the filterless when a foreground outlier is present.10