Background Subtraction Using Ensembles of Classifiers with an ...

Updating the background image via Equation 2.3 offers a solution to not having an adaptivebackground. However a major problem still exists because the algorithm still relies heavily on τ.A poor selection of τ will either result in many false positives or false negatives. One solutionthat eliminates the usage of the rather arbitrary threshold τ is using a Gaussian distribution tomodel each pixel instead of merely the mean or running average, as is demonstrated in [2, 3].Because a pixel is modeled as a Gaussian distribution, foreground detection is based on a frame’scurrent pixel value against the variance.if |I t (p) − B(p)| ≥ σ · k then F G else BG (2.4)In Equation 2.4, now τ = σ · k, where k is some constant, typically around 2. Because thethreshold is directly related to the variance the equation is able to adapt to its environment.In [4] (one of the most influential papers in background modeling), Stauffer et al. first proposedmodeling the background as a combination of multiple Gaussian distributions. This algorithmis referred to the Mixture of Gaussians algorithm. Each pixel p is modeled with a group of KGaussian distributions for each of the red and green color components of p (It is assumed thatthe blue color component is ignored due to its poor reception in human vision), where K is aheuristic value generally set between the values of 3 and 5. The algorithm is first initialized,where a series of image frames are used to train each pixel by clustering the pixel’s observedtraining values into K sets using simple K-means clustering [5]. For each set k ∈ K, the mean(µ k ) and variance (σk 2 ) are computed to parameterize the corresponding Gaussian distribution.Because there are multiple distributions for a single pixel, each distribution k is initially weightedsuch that w(k) = ||k||||K||. There is a future constraint thatK∑w(k) = 1k=1which, holds initially as well.When a new image is processed for segmentation, a pixel is considered to match a particulardistribution if the pixel’s value is within 2.5 standard deviations, where 2.5 is a heuristic thatmay change based on a particular domain. So with K Gaussian distributions, and a pixel historyfor some pixel p = I t (x, y) at time t being {X 1 ...X t }, the probability of observing the pixel X t is:8