1. A method for adapting a boosted classifier to new samples, comprising the steps of: training a boosted classifier using initial samples, in which the boosted classifier is a combination of weak classifiers; updating adaptively, each weak classifier of the boosted classifier, by adding contributions of new samples and deleting contributions of old samples, wherein the samples correspond to foreground pixels and background pixels in frames of a video acquired of a scene by a camera; and tracking an object corresponding to the foreground pixels in the video, wherein a matrix X includes N samples, each sample being a feature vector x, and corresponding labels for the samples are stored in a vector yε{−1, 1} N , in which the labels indicate a binary classification of the samples, and a linear relation X{tilde over (β)}=y is solved by a weighted least squares method, which minimizes an error function 
 (y−X{tilde over (β)}) T W(y−X{tilde over (β)}), where T is a transpose operator, and W is a diagonal matrix constructed from a weight vector w for the samples, and linear coefficients {tilde over (β)} that minimizes the error are given by 
 {tilde over (β)}=(X T WX) −1 X T Wy, which represents a probability distribution on the samples used by the boosted classifier, and wherein the steps are performed in a computer system.