In this work, we present a framework for tracking objects in changing views by finding the subwindow most likely to be the object using Haar-like features selected by AdaBoost as the representation. Probabilistic AdaBoost is used to derive the objective function. In addition, the projective warping of 2D features is used to track 3D objects in non-frontal views in real time. Transformed 2D features can approximate relatively flat object structures such as the two eyes in a face. In this paper, it is shown that, under weak perspective projection, the projective warping of a rectangle feature can be approximated by a similarity tranform with an additional free parameter. Since features in non-frontal views are computed on-the-fly by projective transforms under weak perspective projection, our framework requires only frontal-view training samples to track objects in multiple views.