Background Subtraction Using Ensembles of Classifiers with an ...

CHAPTER 2RELATED WORKS2.1 Early Work in Background ModelingBecause of the computational demands that dynamic background modeling incurs, progress inthe field has mainly been over the past decade, paralleling the explosion of faster computers. Priorto the current robust models, simple background estimation was performed where the backgroundwas assumed to be static. In these schemes an algorithm similar to the one in Equation 2.1 wasused, where B(p) is the estimated background pixel value at pixel p, n is the number of framesused to build the background model, and I t (p) is the value of pixel p at time t.B(p) =n∑i=0I t (p)n(2.1)For a future frame t, pixel p is then predicted to be foreground (FG) or background (BG)based on Equation 2.2, where τ is the threshold (typically set around 50).if |I t (p) − B(p)| ≥ τ then F G else BG (2.2)The largest advantage of this technique is that it is fast and has very low memory requirementsbecause the images may be deleted after the average is calculated. The major problem with suchan algorithm is that it does not adapt to its environment, and it does not have the ability todetect dynamic background regions. The failure to adapt is eliminated if a running average istaken instead. The running average uses a learning rate α (where 0 < α < 1), and updates thebackground model with a new frame I new using Equation 2.3:B new (p) = α · I new (p) + (1 − α) · B old (p) (2.3)7